Climate Change AI Workshop Papers
Tackling Climate Change with Machine Learning
Climate Change AI
ICLR 2024 Mapping Land Naturalness from Sentinel-2 using Deep Contextual and Geographical Priors (Papers Track) Abstract and authors: (click to expand)

Abstract: In recent decades, the causes and consequences of climate change have accelerated, affecting our planet on an unprecedented scale. This change is closely tied to the ways in which humans alter their surroundings. As our actions continue to impact natural areas, using satellite images to see and measure these effects has become crucial for understanding and fighting climate change. Aiming to map land naturalness on the continuum of modern human pressure, we develop a multi-modal supervised deep learning framework that addresses the unique challenges of satellite data and the task at hand. We incorporate contextual and geographical priors. These priors are represented by corresponding coordinate information and broader contextual information including and surrounding the immediate patch to be predicted. Our framework improves the model's predictive performance to map land naturalness from a given Sentinel-2 data, a multi-spectral optical satellite imagery. Recognizing that our protective measures are as effective as our grasp of the ecosystem, quantifying naturalness serves as a crucial step towards enhancing our environmental stewardship.

Authors: Burak Ekim (University of the Bundeswehr); Michael Schmitt (University of the Bundeswehr Munich)

Ecosystems & Biodiversity Forests ICLR 2024 Structured spectral reconstruction for scalable soil organic carbon inference (Papers Track) Abstract and authors: (click to expand)

Abstract: Measuring soil organic carbon (SOC) inexpensively and accurately is crucial for soil health monitoring and agricultural decarbonization. Hyperspectral imaging is commonly evaluated as an inexpensive alternative to dry combustion for SOC measurement, but existing end-to-end approaches trained to predict SOC content from spectral data frequently fail to generalize when applied outside of their ground-truth geographic sampling distributions. Using stratified data from the USDA Rapid Carbon Assessment (RaCA), we demonstrate a method to improve model generalization out-of-distribution by training SOC regression alongside models that reconstruct input spectra. Because hyperspectra can be collected from remote platforms such as drones and satellites, this approach raises the possibility of using large hyperspectral Earth observation datasets to transfer SOC inference models to remote geographies where geographically-dense ground-truth data collection may be expensive or impossible. By replacing the decoder with a simple physics-informed model, we also learn an interpretable spectral signature of SOC, confirming its dark hue and expected reflectance troughs. Finally, we show that catastrophic generalization failures can be better addressed with these architectures by fine-tuning on large quantities of hyperspectral data.

Authors: Evan A Coleman (MIT); Sujay Nair (Georgia Institute of Technology); Xinyi Zeng (Coho Climate Advisors); Elsa Olivetti (MIT Department of Materials Science & Engineering)

Earth Observation & Monitoring Computer Vision & Remote Sensing ICLR 2024 ClimateQ&A : bridging the gap between climate scientists and the general public (Papers Track) Abstract and authors: (click to expand)

Abstract: This research paper investigates public views on climate change and biodiversity loss by analyzing questions asked to the ClimateQ&A platform. ClimateQ&A is a conversational agent that uses LLMs to respond to queries based on over 14,000 pages of scientific literature from the IPCC and IPBES reports. Launched online in March 2023, the tool has gathered over 30,000 questions, mainly from a French audience. Its chatbot interface allows for the free formulation of questions related to nature*. While its main goal is to make nature science more accessible, it also allows for the collection and analysis of questions and their themes. Unlike traditional surveys involving closed questions, this novel method offers a fresh perspective on individual interrogations about nature. Running NLP clustering algorithms on a sample of 3,425 questions, we find that a significant 25.8% inquire about how climate change and biodiversity loss will affect them personally (e.g., where they live or vacation, their consumption habits) and the specific impacts of their actions on nature (e.g., transportation or food choices). This suggests that traditional methods of surveying may not identify all existing knowledge gaps, and that relying solely on IPCC and IPBES reports may not address all individual inquiries about climate and biodiversity, potentially affecting public understanding and action on these issues. *Note: we use “nature” as an umbrella term for “climate change” and “biodiversity loss”.

Authors: Natalia de la Calzada (Ekimetrics); Theo Alves Da Costa (Ekimetrics); Annabelle Blangero (Ekimetrics); Nicolas CHESNEAU (EKIMETRICS)

Natural Language Processing Behavioral and Social Science ICLR 2024 Improving Streamflow Predictions with Vision Transformers (Papers Track) Abstract and authors: (click to expand)

Abstract: Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global Long Short-Term Memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, yet acquiring detailed basin attributes remains a challenge. To overcome this, we introduce the Geo-ViT-LSTM model, a novel approach that enriches LSTM predictions by integrating basin attributes derived from remote sensing with a vision transformer. Applied to 531 basins across the United States (US), our method significantly outperforms existing models, showing an 11% increase in prediction accuracy. Geo-ViT-LSTM marks a significant advancement in land surface modeling, providing a more comprehensive and effective tool for managing water resources under climate change.

Authors: Kshitij Tayal (Oak Ridge National Lab); Arvind Renganathan (University of Minnesota); Dan Lu (Oak Ridge National Laboratory)

Oceans & Marine Systems Time-series Analysis ICLR 2024 Grapevine Disease Prediction Using Climate Variables from Multi-Sensor Remote Sensing Imagery via a Transformer Model (Papers Track) Abstract and authors: (click to expand)

Abstract: Early detection and management of grapevine diseases are important in pursuing sustainable viticulture. This paper introduces a novel framework leveraging the TabPFN model to forecast blockwise grapevine diseases using climate variables from multi-sensor remote sensing imagery. By integrating advanced machine learning techniques with detailed environmental data, our approach significantly enhances the accuracy and efficiency of disease prediction in vineyards. The TabPFN model's experimental evaluations showcase comparable performance to traditional gradient-boosted decision trees, such as XGBoost, CatBoost, and LightGBM. The model's capability to process complex data and provide per-pixel disease-affecting probabilities enables precise, targeted interventions, contributing to more sustainable disease management practices. Our findings underscore the transformative potential of combining Transformer models with remote sensing data in precision agriculture, offering a scalable solution for improving crop health and productivity while reducing environmental impact.

Authors: Weiying Zhao (Deep Planet); Natalia Efremova (Queen Mary University London)

Agriculture & Food Earth Observation & Monitoring Causal & Bayesian Methods ICLR 2024 Towards Ecological Network Analysis with Gromov-Wasserstein Distances (Papers Track) Abstract and authors: (click to expand)

Abstract: Climate change is driving the widespread redistribution of species with cascading effects on predators and their prey. Formally comparing ecological interaction networks is a critical step towards understanding the impact of climate change on ecosystem functioning, yet current methods for ecological network analysis are unable to do so. We propose using the GromovWasserstein (GW) metric for quantifying dissimilarity between ecological networks. We demonstrates the corresponding optimal transport plans of this distance can be interpreted as species functional alignment between food webs. Our results show that GW transport plans align species from different mammal communities consistent with ecological understanding. Furthermore, we illustrate extensions of the GW distance to notions of averages and factorization over ecological networks. Ultimately, we propose the foundation for a novel interpretable topological data analysis framework to inform future ecological research and conservation management.

Authors: Kai M Hung (Rice University); Ann Finneran (Rice University); Alex Zalles (Rice University); Lydia Beaudrot (Rice University); Cesar Uribe (Rice University)

Ecosystems & Biodiversity ICLR 2024 Scaling Transformers for Skillful and Reliable Medium-range Weather Forecasting (Papers Track) Overall Best Paper Abstract and authors: (click to expand)

Abstract: Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it difficult to understand what truly contributes to their success. Here we introduce Stormer, a simple transformer model that achieves state-of-the-art performance on weather forecasting with minimal changes to the standard transformer backbone. We identify the key components of Stormer through careful empirical analyses, including weather-specific embedding, randomized dynamics forecast, and pressure-weighted loss. At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals. During inference, this allows us to produce multiple forecasts for a target lead time and combine them to obtain better forecast accuracy. On WeatherBench 2, Stormer performs competitively at short to medium-range forecasts and outperforms current methods beyond 7 days, while requiring orders-of-magnitude less training data and compute. Additionally, we demonstrate Stormer’s favorable scaling properties, showing consistent improvements in forecast accuracy with increases in model size and training tokens.

Authors: Tung Nguyen (University of California, Los Angeles); Rohan Shah (Carnegie Mellon University); Hritik Bansal (UCLA); Troy Arcomano (Argonne National Laboratory); Sandeep Madireddy (Argonne National Laboratory); Romit Maulik (Argonne National Laboratory); Veerabhadra Kotamarthi (Argonne National Laboratory); Ian Foster (Computation Institute); Aditya Grover (UCLA)

Climate Science & Modeling Extreme Weather ICLR 2024 Categorization of Meteorological Data by Contrastive Clustering (Papers Track) Abstract and authors: (click to expand)

Abstract: Visualized ceilometer backscattering data, displaying meteorological phenomena like clouds, precipitation, and aerosols, is mostly analyzed manually by meteorology experts. In this work, we present an approach for the categorization of backscattering data using a contrastive clustering approach, incorporating image and spatiotemporal information into the model. We show that our approach leads to meteorologically meaningful clusters, opening the door to the automatic categorization of ceilometer data, and how our work could potentially create insights in the field of climate science.

Authors: Michael Dammann (HAW Hamburg); Ina Mattis (DWD); Michael Neitzke (HAW Hamburg); Ralf Möller (University of Lübeck)

Unsupervised & Semi-Supervised Learning Climate Science & Modeling Computer Vision & Remote Sensing ICLR 2024 AI-driven emulation of ocean dynamics on sub-seasonal scales (Papers Track) Abstract and authors: (click to expand)

Abstract: Climate forecasting systems rely on coupling atmospheric models to ocean and sea ice models. However, while there have recently been significant efforts to accelerate atmospheric models using AI, there have been very scarce efforts to accelerate the latter. As a result, climate forecasting systems still rely on expensive numerical simulations, which renders large-scale ensembling and probabilistic prediction cumbersome. To address this issue, we propose a large-scale AI model of ocean dynamics. Our method relies on a spherical neural operator to accurately capture the functional nature of ocean dynamics on the sphere. We empirically demonstrate that our model can accurately predict ocean dynamics for sub-seasonal horizons and outperforms the existing method. It offers a 60x speedup over the fastest numerical solver currently used for the task.

Authors: Suyash Bire (Massachusetts Institute of Technology); Jean Kossaifi (NVIDIA); Simone Silvestri (Massachusetts Institute of Technology); Nikola Kovachki (Nvidia Corp.); Kamyar Azizzadenesheli (Nvidia Corp.); Chris N Hill (MIT); Animashree Anandkumar (Caltech)

Climate Science & Modeling Generative Modeling ICLR 2024 Calibrating Bayesian UNet++ for Sub-seasonal Forecasting (Papers Track) Abstract and authors: (click to expand)

Abstract: Seasonal forecasting is a crucial task when it comes to detecting the extreme heat and colds that occur due to climate change. Confidence in the predictions should be reliable since a small increase in the temperatures in a year has a big impact on the world. Calibration of the neural networks provides a way to ensure our confidence in the predictions. However, calibrating regression models is an under-researched topic, especially in forecasters. We calibrate a UNet++ based architecture, which was shown to outperform physics-based models in temperature anomalies. We show that with a slight trade-off between prediction error and calibration error, it is possible to get more reliable and sharper forecasts. We believe that calibration should be an important part of safety-critical machine learning applications such as weather forecasters.

Authors: Büşra Asan (Istanbul Technical University); Abdullah Akgül (University of Southern Denmark); Alper Unal (Istanbul Technical University); Melih Kandemir (University of Southern Denmark); Gozde Unal (Istanbul Technical University)

Uncertainty Quantification & Robustness Climate Science & Modeling ICLR 2024 Extreme Precipitation Nowcasting using Transformer-based generative models (Papers Track) Abstract and authors: (click to expand)

Abstract: This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, specifically VideoGPT with Extreme Value Loss (EVL) regularization. Leveraging a comprehensive dataset from the Royal Netherlands Meteorological Institute (KNMI), our study focuses on predicting short-term precipitation with high accuracy. We introduce a novel method for computing EVL without assuming fixed extreme representations, addressing the limitations of current models in capturing extreme weather events. We present both qualitative and quantitative analyses, demonstrating the superior performance of the proposed VideoGPT-EVL in generating accurate precipitation forecasts, especially when dealing with extreme precipitation events.

Authors: Cristian Meo (TUDelft); Mircea T Lica (Delft University of Technology); Ankush Roy (TUDelft); Zeina Boucher (TUDelft); Junzhe Yin (TUDelft); Yanbo Wang (Delft University of Technology); Ruben Imhoff (Deltares); Remko Uijlenhoet (TUDelft); Justin Dauwels (TU Delft)

Generative Modeling Climate Science & Modeling Computer Vision & Remote Sensing Time-series Analysis ICLR 2024 GeoFormer: A Vision and Sequence Transformer-based Approach for Greenhouse Gas Monitoring (Papers Track) Abstract and authors: (click to expand)

Abstract: Air pollution represents a pivotal environmental challenge globally, playing a major role in climate change via greenhouse gas emissions and negatively affecting the health of billions. However, predicting the spatial and temporal patterns of pollutants remains challenging. The scarcity of ground-based monitoring facilities and the dependency of air pollution modeling on comprehensive datasets, often inaccessible for numerous areas, complicate this issue. In this work, we introduce GeoFormer, a compact model that combines a vision transformer module with a highly efficient time-series transformer module to predict surface-level nitrogen dioxide (NO2) concentrations from Sentinel-5P satellite imagery. We train the proposed model to predict surface-level NO2 measurements using a dataset we constructed with Sentinel-5P images of ground-level monitoring stations, and their corresponding NO2concentration readings. The proposed model attains high accuracy (MAE 5.65), demonstrating the efficacy of combining vision and time-series transformer architectures to harness satellite-derived data for enhanced GHG emission insights, proving instrumental in advancing climate change monitoring and emission regulation efforts globally.

Authors: Madhav Khirwar (Independent)

Computer Vision & Remote Sensing Climate Science & Modeling Time-series Analysis ICLR 2024 Paddy Doctor: Open Dataset and Automated Pest Identification Using Pre-trained Deep Learning Models (Papers Track) Abstract and authors: (click to expand)

Abstract: Agriculture is one of the most important industries contributing to several countries’ national income. Timely identification of these pests is crucial for both farmers and agriculture experts to implement effective mitigation methods. Traditionally, farmers employ manual techniques based on their experience and visual inspection to identify paddy pests, but this is highly inefficient, time-consuming, and error-prone. At times, even experienced farmers and agriculture experts might struggle to identify a specific species of pests due to their size, colors similar to leaves, and the extensive variety of identical types. Moreover, farmers often apply a large quantity of pesticide without accurately identifying the exact type of pests and their underlying causes. Therefore, it is increasingly important to automate the process of detection of paddy pests to reduce pesticide usage and subsequently minimize the loss in yield. In this paper, we present an open dataset and deep learning models for automated pest identification in real paddy fields. Our dataset contains 6,062 annotated paddy leaf images across 17 classes (16 pest classes and a normal class). We benchmarked our dataset using six pre-trained models (ResNet34, VGG16, DenseNet121, EfficientNet_v2_m, MobileNet_V3_Large, and SqueezeNet1_0). The experimental results showed that ResNet34 achieved the highest accuracy of 98.31%. We release our dataset and reproducible code in the open source for community use.

Authors: Petchiammal A (Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu); Pandarasamy Arjunan (Indian Institute of Science, Bangalore)

Agriculture & Food ICLR 2024 Using expired weather forecasts to supply 10 000y of data for accurate planning of a renewable European energy system (Papers Track) Abstract and authors: (click to expand)

Abstract: Expanding renewable energy generation and electrifying heating to address climate change will heighten the exposure of our power systems to the variability of weather. Planning and assessing these future systems typically lean on past weather data. We spotlight the pitfalls of this approach---chiefly its reliance on what we claim is a limited weather record---and propose a novel approach: to evaluate these systems on two orders of magnitude more weather scenarios. By repurposing past ensemble weather predictions, we not only drastically expand the known weather distribution---notably its extreme tails---for traditional power system modeling but also unveil its potential to enable data-intensive self-supervised, diffusion-based and optimization ML techniques. Building on our methodology, we introduce a **dataset** collected from ECMWF ENS forecasts, encompassing power-system relevant variables over Europe, and detail the intricate process behind its assembly.

Authors: Petr Dolezal (AI4ER CDT, University of Cambridge); Emily Shuckburgh (University of Cambridge)

Power & Energy Climate Science & Modeling Data Mining Uncertainty Quantification & Robustness ICLR 2024 Black carbon plumes from gas flaring in North Africa identified from multi-spectral imagery with deep learning (Papers Track) Abstract and authors: (click to expand)

Abstract: Black carbon (BC) is an important pollutant aerosol emitted by numerous human activities, including gas flaring. Improper combustion in flaring activities can release large amounts of BC, which is harmful to human health and has a strong climate warming effect. To our knowledge, no study has ever directly monitored BC emissions from satellite imagery. Previous works quantified BC emissions indirectly, by applying emission coefficients to flaring volumes estimated from satellite imagery. Here, we develop a deep learning framework and apply it to Sentinel-2 imagery over North Africa during 2022 to detect and quantify BC emissions from gas flaring. We find that BC emissions in this region amount to about 1 million tCO2eq, or 1 million passenger cars, more than a quarter of which are due to 10 sites alone. This work demonstrates the operational monitoring of BC emissions from flaring, a key step in implementing effective mitigation policies to reduce the climate impact of oil and gas operations.

Authors: Alexandre Tuel (Galeio); Thomas Kerdreux (INRIA/ ENS); Louis THIRY (ENS Paris)

Earth Observation & Monitoring Computer Vision & Remote Sensing ICLR 2024 Time-Varying Constraint-Aware Reinforcement Learning for Energy Storage Control (Papers Track) Abstract and authors: (click to expand)

Abstract: Energy storage devices, such as batteries, thermal energy storages, and hydrogen systems, can help mitigate climate change by ensuring a more stable and sustainable power supply. To maximize the effectiveness of such energy storage, determining the appropriate charging and discharging amounts for each time period is crucial. Reinforcement learning is preferred over traditional optimization for the control of energy storage due to its ability to adapt to dynamic and complex environments. However, the continuous nature of charging and discharging levels in energy storage poses limitations for discrete reinforcement learning, and time-varying feasible charge-discharge range based on state of charge (SoC) variability also limits the conventional continuous reinforcement learning. In this paper, we propose a continuous reinforcement learning approach that takes into account the time-varying feasible charge-discharge range. An additional objective function was introduced for learning the feasible action range for each time period, supplementing the objectives of training the actor for policy learning and the critic for value learning. This actively promotes the utilization of energy storage by preventing them from getting stuck in suboptimal states, such as continuous full charging or discharging. This is achieved through the enforcement of the charging and discharging levels into the feasible action range. The experimental results demonstrated that the proposed method further maximized the effectiveness of energy storage by actively enhancing its utilization.

Authors: Jaeik Jeong (Electronics and Telecommunications Research Institute (ETRI)); Tai-Yeon Ku (Electronics and Telecommunications Research Institute (ETRI)); Wan-Ki Park (Electronics and Telecommunications Research Institute (ETRI))

Power & Energy Reinforcement Learning Time-series Analysis ICLR 2024 Towards a Data-Driven Understanding of Cloud Structure Formation (Papers Track) Abstract and authors: (click to expand)

Abstract: The physics of cloud formation and evolution is still not fully understood and constitutes one of the highest uncertainties in climate modeling. We are working on an approach that aims at improving our understanding of how clouds of different structures form from a data-driven perspective: By predicting the visual appearance of cloud photographs from physical quantities obtained from reanalysis data and subsequently attributing the decisions to physical quantities using ``explainable AI'' methods, we try to identify relevant physical processes. At the current stage, this is just a proof of concept, being at least able to identify basic meteorologically plausible facts from data.

Authors: Ann-Christin Wörl (Johannes Gutenberg University); Michael Wand (University of Mainz); Peter Spichtinger (Johannes Gutenberg University)

Interpretable ML Climate Science & Modeling ICLR 2024 Bee Activity Prediction and Pattern Recognition in Environmental Data (Papers Track) Abstract and authors: (click to expand)

Abstract: As a consequence of climate change, biodiversity is declining rapidly. Many species like insects, especially bees, suffer from changes in temperature and rainfall patterns. Applying machine learning for monitoring and predicting specie's health and life conditions can help understanding and improving biodiversity. In this work we use data collected from cameras and sensors mounted upon beehives together with different other data sources like weather data, information extracted from satellite images and geographical information. We aim at predicting bees' health (measured as their activity) and analyzing influencing environmental conditions. We show that we are able to accurately predict bees' activity and understand their life conditions by using machine learning algorithms and explainable AI. Understanding these conditions can help to make recommendations on good locations for beehives. This work illustrates the potential of applying machine learning on sensor, satellite and weather data for monitoring and predicting species' health and hence shows the ability for adaptation to climate change and a more accurate species monitoring.

Authors: Christine Preisach (University of Applied Sciences Karlsruhe); Marius Herrmann (Karlsruhe Institute of Technology)

Ecosystems & Biodiversity Interpretable ML ICLR 2024 Advancing Earth System Model Calibration: A Diffusion-Based Method (Papers Track) Honorable Mention Abstract and authors: (click to expand)

Abstract: Understanding of climate impact on ecosystems globally requires site-specific model calibration. Here we introduce a novel diffusion-based uncertainty quantification (DBUQ) method for efficient model calibration. DBUQ is a score-based diffusion model that leverages Monte Carlo simulation to estimate the score function and evaluates a simple neural network to quickly generate samples for approximating parameter posterior distributions. DBUQ is stable, efficient, and can effectively calibrate the model given diverse observations, thereby enabling rapid and site-specific model calibration on a global scale. This capability significantly advances Earth system modeling and our understanding of climate impacts on Earth systems. We demonstrate DBUQ's capability in E3SM land model calibration at the Missouri Ozark AmeriFlux forest site. Both synthetic and real-data applications indicate that DBUQ produces accurate parameter posterior distributions similar to those generated by Markov Chain Monte Carlo sampling but with 30X less computing time. This efficiency marks a significant stride in model calibration, paving the way for more effective and timely climate impact analyses.

Authors: Yanfang Liu (Oak Ridge National Laboratory); Dan Lu (Oak Ridge National Laboratory); Zezhong Zhang (Oak Ridge National Laboratory); Feng Bao (Florida State University); Guannan Zhang (Oak Ridge National Laboratory)

Generative Modeling Ecosystems & Biodiversity Uncertainty Quantification & Robustness ICLR 2024 Towards Downscaling Global AOD with Machine Learning (Papers Track) Abstract and authors: (click to expand)

Abstract: Poor air quality represents a significant threat to human health, especially in urban areas. To improve forecasts of air pollutant mass concentrations, there is a need for high-resolution Aerosol Optical Depth (AOD) forecasts as proxy. However, current General Circulation Model (GCM) forecasts of AOD suffer from limited spatial resolution, making it difficult to accurately represent the substantial variability exhibited by AOD at the local scale. To address this, a deep residual convolutional neural network (ResNet) is evaluated for the GCM to local scale downscaling of low-resolution global AOD retrievals, outperforming a non-trainable interpolation baseline. We explore the bias correction potential of our ResNet using global reanalysis data, evaluating it against in-situ AOD observations. The improved resolution from our ResNet can assist in the study of local AOD variations.

Authors: Josh Millar (Imperial College London); Paula Harder (Mila); Lilli J Freischem (University of Oxford); Philipp Weiss (University of Oxford); Philip Stier (University of Oxford)

Climate Science & Modeling Computer Vision & Remote Sensing ICLR 2024 Reconstructing the Breathless Ocean with Spatio-Temporal Graph Learning (Papers Track) Abstract and authors: (click to expand)

Abstract: The ocean is currently undergoing severe deoxygenation. Accurately reconstructing the breathless ocean is crucial for assessing and protecting marine ecosystem in response to climate change. Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. Besides, due to the high-cost data collection, the historical observations are severely sparse, leading to big challenge for precise reconstruction. In this work, we propose OxyGenerator, the first spatio-temporal graph learning model, to reconstruct the global ocean deoxygenation from 1920 to 2023. Specifically, to address the heterogeneity across large temporal and spatial scales, we propose zoning-varying graph message-passing to capture the complex oceanographic correlations between missing values and sparse observations. Additionally, to further calibrate the uncertainty, we incorporate inductive bias from dissolved oxygen (DO) variations and chemical effects. Compared with in-situ DO observations, OxyGenerator significantly outperforms CMIP6 numerical simulations, reducing MAPE by 38.77%, demonstrating a promising potential to understand the ocean deoxygenation in data-driven manner.

Authors: Bin Lu (Shanghai Jiao Tong University); Ze Zhao (Shanghai Jiao Tong University); Luyu Han (Shanghai Jiao Tong University); Xiaoying Gan (Shanghai Jiao Tong University); Yuntao Zhou (Shanghai Jiao Tong University); Lei Zhou (Shanghai Jiao Tong Univ); Luoyi Fu (Shanghai Jiao Tong University); Xinbing Wang (Shanghai Jiao Tong University); Chenghu Zhou (Institute of Geographic Sciences and Natural Resources Research, CAS); Jing Zhang (Shanghai Jiao Tong University)

Oceans & Marine Systems Data Mining Time-series Analysis ICLR 2024 Analyzing the secondary wastewater-treatment process using Faster R-CNN and YOLOv5 object detection algorithms (Papers Track) Abstract and authors: (click to expand)

Abstract: The activated sludge (AS) process is the most common type of secondary wastewater treatment, applied worldwide. Due to the complexity of microbial communities, imbalances between the different types of bacteria may occur and disturb the process, with pronounced economical and environmental consequences. Microscopic inspection of the morphology of flocs and microorganisms provides key information on AS properties and function. This is a time-consuming, highly skilled, and expensive process that is not readily available in all locations. Thus, most wastewater-treatment plants do not carry out this essential analysis, resulting in frequent operational faults. In this study, we develop a novel deep learning (DL) object detection algorithm to analyze and monitor the AS process based on a unique microscopic image database of flocs and microorganisms. Specifically, we applied YOLOv5 and Faster R-CNN algorithms as tools for segmentation and object detection to analyze the wastewater. The mean average precision (mAP) of the YOLOv5 was 0.67, outperforming the Faster R-CNN by 15%. Histogram equalization preprocessing of both bright-field and phase-contrast images significantly improved the results of the algorithm in all classes. In the case of YOLOv5, the mAP increased by 16.67%, to 0.77, where the AP of protozoa, filaments, and open floc classes outperformed the previous model by over 20%. These results demonstrate the potential of leveraging DL algorithms to enhance the analysis and monitoring of WWTPs in an affordable manner, consequently reducing environmental pollution caused by contaminated effluent. The fundamental challenge addressed herein has important global relevance, especially in an era in which the demand for high-quality wastewater reuse is expected to increase dramatically.

Authors: Offir Inbar (Tel-Aviv University); Moni Shahar (Tel Aviv University); Jacob Gidron (Tel-Aviv University); Ido Cohen (Tel-Aviv University); Dror Avisar (Tel-Aviv University)

Agriculture & Food Cities & Urban Planning Ecosystems & Biodiversity Health Chemistry & Materials Computer Vision & Remote Sensing ICLR 2024 Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models (Papers Track) Abstract and authors: (click to expand)

Abstract: Earth system models (ESMs) are the principal tools used in climate science to generate future climate projections under various atmospheric emissions scenarios on a global or regional scale. Generative deep learning approaches are suitable for emulating these tools due to their computational efficiency and ability, once trained, to generate realizations in a fraction of the time required by ESMs. We extend previous work that used a generative probabilistic diffusion model to emulate ESMs by targeting the joint emulation of multiple variables, temperature and precipitation, by a single diffusion model. Joint generation of multiple variables is critical to generate realistic samples of phenomena resulting from the interplay of multiple variables. The diffusion model emulator takes in the monthly mean-map of temperature and precipitation and produces the daily values of each of these variables that exhibit statistical properties similar to those generated by ESMs. Our results show the outputs from our extended model closely resemble those from ESMs on various climate metrics including dry spells and hot streaks, and that the joint distribution of temperature and precipitation in our sample closely matches those of ESMs.

Authors: Katie Christensen (Western Washington University); Lyric Otto (Western Washington University); Seth Bassetti (Utah State University); Claudia Tebaldi (Joint Global Change Research Institute); Brian Hutchinson (Western Washington University)

Climate Science & Modeling Generative Modeling ICLR 2024 Verifying Practices of Regenerative Agriculture: African Smallholder Farmer Dataset for Remote Sensing and Machine Learning (Papers Track) Abstract and authors: (click to expand)

Abstract: Despite Africa’s contribution to global greenhouse gas (GHG) emissions being only a few %, the continent experiences the harshest impacts, particularly within its food production systems. Regenerative agriculture is receiving a large amount of attention as a method to strengthen both food security and climate change resilience in Africa. For practicing regenerative agriculture, carbon credits are issued, but verifying the methodologies on a large scale is one of the challenging points in popularizing it. In this paper, we provide a comprehensive dataset on regenerative agriculture in sub-Saharan Africa. The dataset has field polygon information and is labeled with several types of regenerative agriculture methodologies. The dataset can be applied to local site analysis, classification, and detection of regenerative agriculture with remote sensing and machine learning. We also highlight several machine learning models and summarize the baseline results on our dataset. We believe that by providing this dataset, we can contribute to the establishment of verification methods for regenerative agriculture. The dataset can be downloaded from https://osf.io/xgp9m/.

Authors: Yohei Nakayama (Degas Ltd); Grace Antwi (Degas Ltd); Seiko Shirasaka (Keio University)

Agriculture & Food Computer Vision & Remote Sensing ICLR 2024 Semi-Supervised Domain Adaptation for Wildfire Detection (Papers Track) Abstract and authors: (click to expand)

Abstract: Recently, both the frequency and intensity of wildfires have increased worldwide, primarily due to climate change. In this paper, we propose a novel protocol for wildfire detection, leveraging semi-supervised Domain Adaptation for object detection, accompanied by a corresponding dataset designed for use by both academics and industries. Our dataset encompasses 30 times more diverse labeled scenes than the current largest benchmark wildfire dataset, HPWREN, and introduces a new labeling policy for wildfire detection. Inspired by coordconv., we propose a robust baseline, Location-Aware Object Detection for Semi-Supervised Domain Adaptation (LADA), utilizing a teacher-student based self-supervised Domain Adaptation framework capable of extracting translational variance features characteristic of wildfires. Our framework significantly outperforms the existing baseline by a notable margin of 3.8\%p in mean Average Precision on the HPWREN wildfire dataset.

Authors: Joo Young Jang (Alchera); Youngseo Cha (Alchera); Jisu Kim (Alchera); SooHyung Lee (Alchera); Geonu Lee (Alchera); Minkook Cho (Alchera); Young Hwang (Alchera); Nojun Kwak (Seoul National University)

Unsupervised & Semi-Supervised Learning Disaster Management and Relief Computer Vision & Remote Sensing ICLR 2024 Identifying Climate Targets in National Laws and Policies using Machine Learning (Papers Track) Abstract and authors: (click to expand)

Abstract: Quantified policy targets are a fundamental element of climate policy, typically characterised by domain-specific and technical language. Current methods for curating comprehensive views of global climate policy targets entail significant manual effort. At present there are few scalable methods for extracting climate targets from national laws or policies, which limits policymakers’ and researchers’ ability to (1) assess private and public sector alignment with global goals and (2) inform policy decisions. In this paper we present an approach for extracting mentions of climate targets from national laws and policies. We create an expert-annotated dataset identifying three categories of target (’Net Zero’, ’Reduction’ and ’Other’ (e.g. renewable energy targets)) and train a classifier to reliably identify them in text. We investigate bias and equity impacts related to our model and identify specific years and country names as problematic features. We explore the dataset generated from applying our classifier to the Climate Policy Radar (CPR) dataset, showcasing the potential for automated data collection and research support in climate policy. Our work represents a significant upgrade in the accessibility of these key climate policy elements for policymakers and researchers.

Authors: Matyas Juhasz (Climate Policy Radar); Tina Marchand (Climate Policy Radar); Roshan Melwani (Climate Policy Radar); Kalyan Dutia (Climate Policy Radar); Sarah Goodenough (Climate Policy Radar); Harrison Pim (Climate Policy Radar); Henry Franks (Climate Policy Radar)

Public Policy ICLR 2024 Empowering Sustainable Finance: Leveraging Large Language Models for Climate-Aware Investments (Papers Track) Abstract and authors: (click to expand)

Abstract: With the escalating urgency of climate change, it is becoming more imperative for businesses and organizations to align their objectives with sustainability goals. Financial institutions also face a critical mandate to fulfill the Sustainable Development Goals (SDGs), particularly goal 13, which targets the fight against climate change and its consequences. Mitigating the impacts of climate change requires a focus on reducing supply chain emissions, which constitute over 90% of total emission inventories. In the financial industry, supply chain emissions linked to lending and investments emerge as the primary source of emissions, posing challenges in tracking financed emissions due to the intricate process of collecting data from numerous suppliers across the supply chain. To address these challenges, we propose an emission estimation framework utilizing a Large Language Model (LLM) to drastically accelerate the assessment of the emissions associated with lending and investment activities. This framework utilizes financial activities as a proxy for measuring financed emissions. Utilizing the LLM, we classify financial activities into seven asset classes following the Partnership for Carbon Accounting Financials (PCAF) standard. Additionally, we map investments to industry categories and employ spend-based emission factors (kg-CO2/$-spend) to calculate emissions associated with financial investments. In our study, we compare the performance of our proposed method with state-of-the-art text classification models like TF-IDF, word2Vec, and Zero-shot learning. The results demonstrate that the LLM-based approach not only surpasses traditional text mining techniques and performs on par with a subject matter expert (SME) but most importantly accelerates the assessment process.

Authors: Ayush Jain (IBM Research); Manikandan Padmanaban (IBM Research India); Jagabondhu Hazra (IBM Research India); Shantanu Godbole (IBM India); Hendrik Hamann (IBM Research)

Natural Language Processing Supply Chains ICLR 2024 Near-real-time monitoring of global ocean carbon sink (Papers Track) Abstract and authors: (click to expand)

Abstract: The ocean, absorbing about 25% of anthropogenic CO2 emissions, plays a crucial role in mitigating climate change. However, the delayed (by one year) traditional estimates of ocean-atmosphere CO2 flux hinder timely understanding and response to the global carbon cycle’s dynamics. Addressing this challenge, we introduce Carbon Monitor Ocean (CMO-NRT), a pioneering dataset providing near-real-time, monthly gridded estimates of global surface ocean fugacity of CO2 (fCO2) and ocean-atmosphere CO2 flux from January 2022 to July 2023. This dataset marks a significant advancement by updating the global carbon budget’s estimates through a fusion of data from 10 Global Ocean Biogeochemical Models (GOBMs) and 8 data products into a near-real-time analysis framework. By harnessing the power of Convolutional Neural Networks (CNNs) and semi-supervised learning techniques, we decode the complex nonlinear relationships between model or product estimates and observed environmental predictors. The predictive models, both for GOBM and data products, exhibit exceptional accuracy, with root mean square errors (RMSEs) maintaining below the 5% threshold. This advancement supports more effective climate change mitigation efforts by providing scientists and policymakers with timely and accurate data.

Authors: Xiaofan Gui (Microsoft Research); Jiang Bian (Microsoft Research)

Climate Science & Modeling Unsupervised & Semi-Supervised Learning ICLR 2024 WindDragon: enhancing wind power forecasting with automated deep learning (Papers Track) Abstract and authors: (click to expand)

Abstract: Achieving net zero carbon emissions by 2050 requires the integration of increasing amounts of wind power into power grids. This energy source poses a challenge to system operators due to its variability and uncertainty. Therefore, accurate forecasting of wind power is critical for grid operation and system balancing. This paper presents an innovative approach to short-term (1 to 6 hour horizon) wind power forecasting at a national level. The method leverages Automated Deep Learning combined with Numerical Weather Predictions wind speed maps to accurately forecast wind power.

Authors: Julie Keisler (INRIA, EDF R&D); Etienne Le Naour (Sorbonne University, EDF R&D)

Power & Energy ICLR 2024 Neural Processes for Short-Term Forecasting of Weather Attributes (Papers Track) Abstract and authors: (click to expand)

Abstract: Traditional weather prediction models rely on solving complex physical equations, with long computation time. Machine learning models can process large amount of data more quickly. We propose to use neural processes (NPs) for short-term weather attributes forecasting. This is a novel avenue of research, as previous work has focused on NPs for long-term forecasting. We compare a multi-task neural process (MTNP) to an ensemble of independent single-task NPs (STNP) and to an ensemble of Gaussian processes (GPs). We use time series data for multiple weather attributes from Chichester Harbour over a one-week period. We evaluate performance in terms of NLL and MSE with 2-hours and 6-hours time horizons. When limited context information is provided, the MTNP leverages inter-task knowledge and outperforms the STNP. The STNP outperforms both the MTNP and the GPs ensemble when a sufficient, but not exceeding, amount of context information is provided.

Authors: Benedetta L Mussati (University of Oxford); Helen McKay (Mind Foundry); Stephen Roberts (University of Oxford)

Time-series Analysis Meta- and Transfer Learning ICLR 2024 EU Climate Change News Index: Forecasting EU ETS prices with online news (Papers Track) Abstract and authors: (click to expand)

Abstract: Carbon emission allowance prices have been rapidly increasing in the EU since 2018 and accurate forecasting of EU Emissions Trading System (ETS) prices has become essential. This paper proposes a novel method to generate alternative predictors for daily ETS price returns using relevant online news information. We devise the EU Climate Change News Index by calculating the term frequency–inverse document frequency (TF–IDF) feature for climate change-related keywords. The index is capable of tracking the ongoing debate about climate change in the EU. Finally, we show that incorporating the index in a simple predictive model significantly improves forecasts of ETS price returns.

Authors: Aron Pap (BGSE); Aron D Hartvig (Corvinus University of Budapest, Cambridge Econometrics); Péter Pálos (Budapest University of Technology and Economics)

Climate Finance & Economics Power & Energy Public Policy Natural Language Processing Time-series Analysis ICLR 2024 Explaining Zeolite Synthesis-Structure Relationships using Aggregated SHAP Analysis (Papers Track) Abstract and authors: (click to expand)

Abstract: Zeolites, crystalline aluminosilicate materials with well-defined porous structures, have emerged as versatile materials with applications in carbon capture. Hydrothermal synthesis is a widely used method for zeolite production, offering control over crystallinity and and pore size. However, the intricate interplay of synthesis parameters necessitates a comprehensive understanding to optimize the synthesis process. We train a supervised classification machine learning model on ZeoSyn (a dataset of zeolite synthesis routes) to predict the zeolite framework product given a synthesis route. Subsequently, we leverage SHapley Additive Explanations (SHAP) to reveal key synthesis-structure relationships in zeolites. To that end, we introduce an aggregation SHAP approach to extend such analysis to explain the formation of composite building units (CBUs) of zeolites. Analysis at this unprecedented scale sheds light on key synthesis parameters driving zeolite crystallization.

Authors: Elton Pan (MIT)

Chemistry & Materials Carbon Capture & Sequestration Interpretable ML ICLR 2024 Fast non-stationary geospatial modelling with multiresolution (wavelet) Gaussian processes (Papers Track) Abstract and authors: (click to expand)

Abstract: Climate modelling tasks involve assimilating large amounts of geospatial data from different sources, such as simulators and measurements from weather stations and satellites. These sources of data are weighted according to their uncertainty, so good quality uncertainty estimates are essential. Gaussian processes (GPs) offer flexible models with uncertainty estimates, and have a long track record of use in geospatial modelling. However, much of the research effort, including recent work on scalability, is focused on statistically stationary models, which are not suitable for many climatic variables, such as precipitation. Here we propose a novel, scalable, nonstationary GP model based upon discrete wavelets, and evaluate them on toy and real world data.

Authors: Talay M Cheema (University of Cambridge); Carl Edward Rasmussen (Cambridge University)

Causal & Bayesian Methods Climate Science & Modeling Generative Modeling ICLR 2024 PostRainBench: A comprehensive benchmark and a new model for precipitation forecasting (Papers Track) Abstract and authors: (click to expand)

Abstract: Accurate precipitation forecasting is a vital challenge of societal importance. Though data-driven approaches have emerged as a widely used solution, solely relying on data-driven approaches has limitations in modeling the underlying physics, making accurate predictions difficult. We focus on the Numerical Weather Prediction (NWP) post-processing based precipitation forecasting task to couple Machine Learning techniques with traditional NWP. This task remains challenging due to the imbalanced precipitation data and complex relationships between multiple meteorological variables. To address these limitations, we introduce the PostRainBench, a comprehensive multi-variable NWP post-processing benchmark, and CAMT, a simple yet effective Channel Attention Enhanced Multi-task Learning framework with a specially designed weighted loss function. Extensive experimental results on the proposed benchmark show that our method outperforms state-of-the-art methods by 6.3%, 4.7%, and 26.8% in rain CSI and improvements of 15.6%, 17.4%, and 31.8% over NWP predictions in heavy rain CSI on respective datasets. Most notably, our model is the first deep learning-based method to outperform NWP approaches in heavy rain conditions. These results highlight the potential impact of our model in reducing the severe consequences of extreme rainfall events. Our datasets and code are available at https://github.com/yyyujintang/PostRainBench.

Authors: Yujin Tang (The Hong Kong University of Science and Technology (Guangzhou)); Jiaming Zhou (Hong Kong University of Science and Technology (Guangzhou)); Xiang Pan (Nanjing University); Zeying Gong (Hong Kong University of Science and Technology (Guangzhou)); Junwei Liang (The Hong Kong University of Science and Technology (Guangzhou))

Extreme Weather Computer Vision & Remote Sensing ICLR 2024 Physics-informed Machine Learning-based Cloud Microphysics parameterization for Earth System Models (Papers Track) Abstract and authors: (click to expand)

Abstract: In this study, we develop a physics-informed machine learning (ML)-based cloud microphysics parameterization for the ICON model. By training the ML parameterization on high-resolution simulation data, we aim to improve Earth System Models (ESMs) in comparison to traditional parameterization schemes. We investigate the usage of a multilayer perceptron (MLP) with feature engineering and physics-constraints, and use explainability techniques to understand the relationship between input features and model output. Our novel approach yields promising results, with the physics-informed ML-based cloud microphysics parameterization achieving an R$^2$ score up to 0.777 for an individual feature. Additionally, we demonstrate a notable improvement in the overall performance in comparison to a baseline MLP, increasing its average R$^2$ score from 0.290 to 0.613 across all variables. This approach to improve the representation of cloud microphysics in ESMs promises to enhance climate projections, contributing to a better understanding of climate change.

Authors: Ellen Sarauer (German Aerospace Center (DLR)); Mierk Schwabe (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany); Philipp Weiss (University of Oxford); Axel Lauer (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany); Philip Stier (University of Oxford); Veronika Eyring (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany; University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany)

Climate Science & Modeling Interpretable ML ICLR 2024 From spectra to biophysical insights: end-to-end learning with a biased radiative transfer model (Papers Track) Abstract and authors: (click to expand)

Abstract: Advances in machine learning have boosted the use of Earth observation data for climate change research. Yet, the interpretability of machine-learned representations remains a challenge, particularly in understanding forests' biophysical reactions to climate change. Traditional methods in remote sensing that invert radiative transfer models (RTMs) to retrieve biophysical variables from spectral data often fail to account for biases inherent in the RTM, especially for complex forests. We propose to integrate RTMs into an auto-encoder architecture, creating an end-to-end learning approach. Our method not only corrects biases in RTMs but also outperforms traditional techniques for variable retrieval like neural network regression. Furthermore, our framework has potential generally for inverting biased physical models.

Authors: Yihang She (University of Cambridge); Clement Atzberger (Mantle Labs); Andrew Blake (University of Cambridge, Mantle Labs); Srinivasan Keshav (University of Cambridge)

Earth Observation & Monitoring Interpretable ML ICLR 2024 Model Failure or Data Corruption? Exploring Inconsistencies in Building Energy Ratings with Self-Supervised Contrastive Learning (Papers Track) Abstract and authors: (click to expand)

Abstract: Building Energy Rating (BER) stands as a pivotal metric, enabling building owners, policymakers, and urban planners to understand the energy-saving potential through improving building energy efficiency. As such, enhancing buildings' BER levels is expected to directly contribute to the reduction of carbon emissions and promote climate improvement. Nonetheless, the BER assessment process is vulnerable to missing and inaccurate measurements. In this study, we introduce CLEAR, a data-driven approach designed to scrutinize the inconsistencies in BER assessments through self-supervised contrastive learning. We validated the effectiveness of CLEAR using a dataset representing Irish building stocks. Our experiments uncovered evidence of inconsistent BER assessments, highlighting measurement data corruption within this real-world dataset.

Authors: Qian Xiao (Trinity College Dublin); Dan Liu (Trinity College Dublin); Kevin Credit (Maynooth University)

Buildings Data Mining Interpretable ML Unsupervised & Semi-Supervised Learning ICLR 2024 Interpretable Machine Learning for Extreme Events detection: An application to droughts in the Po River Basin (Papers Track) Abstract and authors: (click to expand)

Abstract: The increasing frequency and intensity of drought events-periods of significant decrease in water availability-are among the most alarming impacts of climate change. Monitoring and detecting these events is essential to mitigate their impact on our society. However, traditional drought indices often fail to accurately detect such impacts as they mostly focus on single precursors. In this study, we leverage machine learning algorithms to define a novel data-driven, impact-based drought index reproducing as target the Vegetation Health Index, a satellite signal that directly assesses the vegetation status. We first apply novel dimensionality reduction methods that allow for interpretable spatial aggregation of features related to precipitation, temperature, snow, and lakes. Then, we select the most informative and non-redundant features through filter feature selection. Finally, linear supervised learning methods are considered, given the small number of samples and the aim of preserving interpretability. The experimental setting focuses on ten sub-basins of the Po River basin, but the aim is to design a machine learning-based workflow applicable on a large scale.

Authors: Paolo Bonetti (Politecnico di Milano); Matteo Giuliani (Politecnico di Milano); Veronica Cardigliano (Politecnico di Milano); Alberto Maria Metelli (Politecnico di Milano); Marcello Restelli (Politecnico di Milano); Andrea Castelletti (Politecnico di Milano)

Agriculture & Food Extreme Weather Interpretable ML ICLR 2024 Literature Mining with Large Language Models to Assist the Development of Sustainable Building Materials (Papers Track) Abstract and authors: (click to expand)

Abstract: Concrete industry, as one of the significant sources of carbon emissions, drives the urgency for its decarbonization that requires a shift to alternative materials. However, the absence of systematic knowledge summary remains a challenge for further development of sustainable building materials. This work offers a cost-efficient strategy for information extraction tasks in complex terminology settings using small (2.8B) large language models (LLMs) with well-designed instruction-completion schemes and fine-tuning strategies, introducing a dataset cataloging civil engineering applications of alternative materials. The Multiple Choice instruction scheme significantly improves model accuracies in entity inference from non-Noun-Phrase sources, with supervised fine-tuning benefiting from straightforward tokenized representations of choices. We also demonstrate the utility of the dataset by extracting valuable insights into promising applications of alternative materials from knowledge graph representations.

Authors: Yifei Duan (Massachusetts Institute of Technology); Yixi Tian (Massachusetts Institute of Technology); Soumya Ghosh (IBM Research); Richard Goodwin (IBM T.J. Watson Research Center); Vineeth Venugopal (Massachusetts Institute of Technology); Jeremy Gregory (Massachusetts Institute of Technology); Jie Chen (IBM Research); Elsa Olivetti (Massachusetts Institute of Technology)

Natural Language Processing Buildings Chemistry & Materials ICLR 2024 A Deep Learning Technology Suite for Cost-Effective Sequestered CO2 Monitoring (Papers Track) Abstract and authors: (click to expand)

Abstract: Carbon capture and storage (CCS) is a way of reducing carbon emissions to help tackle global warming. Injecting CO2 into rock formations and preventing it from escaping to the surface is a main step in a CCS project. Therefore, monitoring of geologically sequestered CO2 is important for CCS security assessment. Time-lapse seismic (4D seismic) is one of the most effective tools for CO2 monitoring. Unfortunately, the main challenge of 4D seismic is the high cost due to repeated monitoring seismic data acquisition surveys and the subsequent time-consuming data processing that involves imaging and inversion. To address this, we developed a technology suite powered by deep learning engines that significantly reduces the cost by (1) acquiring very sparse monitoring data; (2) firing multiple seismic sources simultaneously; (3) converting 2D images to 3D volume; (4) enforcing repeatability between baseline data and monitoring data; and (5) nonlinearly mapping seismic data to subsurface property model to bypass complex wave-equation-based seismic data processing procedures.

Authors: Wenyi Hu (SLB); Son Phan (SLB); Cen Li (SLB); Aria Abubakar (SLB)

Carbon Capture & Sequestration Earth Observation & Monitoring Climate Science & Modeling Hybrid Physical Models Unsupervised & Semi-Supervised Learning ICLR 2024 On the potential of Optimal Transport in Geospatial Data Science (Papers Track) Abstract and authors: (click to expand)

Abstract: Prediction problems in geographic information science and transportation are often motivated by the possibility to enhance operational efficiency and thereby reduce emissions. Examples range from predicting car sharing demand for relocation planning to forecasting traffic congestion for navigation purposes. However, conventional accuracy metrics ignore the spatial distribution of the errors, despite its relevance for operations. Here, we put forward a spatially aware evaluation metric and loss function based on Optimal Transport (OT). Our framework leverages partial OT and can minimize relocation costs in any spatial prediction problem. We showcase the advantages of OT-based evaluation over conventional metrics and further demonstrate the application of an OT loss function for improving forecasts of bike sharing demand and charging station occupancy. Thus, our framework not only aligns with operational considerations, but also signifies a step forward in refining predictions within geospatial applications. All code is available at https://github.com/mie-lab/geospatial_optimal_transport.

Authors: Nina V Wiedemann (ETH Zurich); Martin Raubal (ETH Zürich)

Time-series Analysis Climate Science & Modeling Transportation ICLR 2024 ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability (Papers Track) Abstract and authors: (click to expand)

Abstract: The Environmental Extended Multi-Regional Input-Output analysis is the predominant Ecological Economic research framework for analysing the environmental impact of economic activities. This paper introduces the novel ExioML dataset as the first Machine Learning benchmark data in sustainability analysis. We open-sourced the ExioML data and development toolkit to lower barriers and accelerate the cooperation between Machine Learning and Ecological Economic research. A crucial greenhouse gas emission regression task evaluates the usability of the proposed dataset. We compared the performance of traditional shallow models against deep models by leveraging a diverse factor accounting table and incorporating multiple modalities of categorical and numerical features. Our findings reveal that deep and ensemble models achieve low mean square errors below 0.25 and serve as a future machine learning research baseline. Through Ex- ioML, we aim to foster precise ML predictions and modelling to support climate actions and sustainable investment decisions. The data and codes are available: https://github.com/Yvnminc/ExioML

Authors: Yanming Guo (University of Sydney)

Climate Finance & Economics ICLR 2024 FARADAY: SYNTHETIC SMART METER GENERATOR FOR THE SMART GRID (Papers Track) Abstract and authors: (click to expand)

Abstract: Access to smart meter data is essential to rapid and successful transitions to elec- trified grids, underpinned by flexibility delivered by low carbon technologies, such as electric vehicles (EV) and heat pumps, and powered by renewable energy. Yet little of this data is available for research and modelling purposes due consumer privacy protections. Whilst many are calling for raw datasets to be unlocked through regulatory changes, we believe this approach will take too long. Synthetic data addresses these challenges directly by overcoming privacy issues. In this pa- per, we present Faraday, a Variational Auto-encoder (VAE)-based model trained over 300 million smart meter data readings from an energy supplier in the UK, with information such as property type and low carbon technologies (LCTs) own- ership. The model produces household-level synthetic load profiles conditioned on these labels, and we compare its outputs against actual substation readings to show how the model can be used for real-world applications by grid modellers interested in modelling energy grids of the future.

Authors: Sheng Chai (Centre for Net Zero); Gus Chadney (Centre for Net Zero)

Generative Modeling ICLR 2024 Generalizable Temperature Nowcasting with Physics-Constrained RNNs for Predictive Maintenance of Wind Turbine Components (Papers Track) Abstract and authors: (click to expand)

Abstract: Machine learning plays an important role in the operation of current wind energy production systems. One central application is predictive maintenance to increase efficiency and lower electricity costs by reducing downtimes. Integrating physics-based knowledge in neural networks to enforce their physical plausibilty is a promising method to improve current approaches, but incomplete system information often impedes their application in real world scenarios. We describe a simple and efficient way for physics-constrained deep learning-based predictive maintenance for wind turbine gearbox bearings with partial system knowledge. The approach is based on temperature nowcasting constrained by physics, where unknown system coefficients are treated as learnable neural network parameters. Results show improved generalization performance to unseen environments compared to a baseline neural network, which is especially important in low data scenarios often encountered in real-world applications.

Authors: Johannes Exenberger (Graz University of Technology); Matteo Di Salvo (Sirius Energy Automation); Thomas Hirsch (Graz University of Technology); Franz Wotawa (Graz University of Technology, Institute for Software Technology); Gerald Schweiger (Graz University of Technology)

Hybrid Physical Models Time-series Analysis ICLR 2024 Forecasting Tropical Cyclones with Cascaded Diffusion Models (Papers Track) Abstract and authors: (click to expand)

Abstract: As tropical cyclones become more intense due to climate change, the rise of Al-based modelling provides a more affordable and accessible approach compared to traditional methods based on mathematical models. This work leverages generative diffusion models to forecast cyclone trajectories and precipitation patterns by integrating satellite imaging, remote sensing, and atmospheric data. It employs a cascaded approach that incorporates three main tasks: forecasting, super-resolution, and precipitation modelling. The training dataset includes 51 cyclones from six major tropical cyclone basins from January 2019 - March 2023. Experiments demonstrate that the final forecasts from the cascaded models show accurate predictions up to a 36-hour rollout, with excellent Structural Similarity (SSIM) and Peak-Signal-To-Noise Ratio (PSNR) values exceeding 0.5 and 20 dB, respectively, for all three tasks. The 36-hour forecasts can be produced in as little as 30 mins on a single Nvidia A30/RTX 2080 Ti. This work also highlights the promising efficiency of Al methods such as diffusion models for high-performance needs in weather forecasting, such as tropical cyclone forecasting, while remaining computationally affordable, making them ideal for highly vulnerable regions with critical forecasting needs and financial limitations. Code accessible at \url{https://github.com/nathzi1505/forecast-diffmodels}.

Authors: Pritthijit Nath (Imperial College London); Pancham Shukla (Imperial College London); Shuai Wang (University of Delaware); Cesar Quilodran-Casas (Imperial College London)

Generative Modeling Extreme Weather ICLR 2024 Forecasting regional PV power in Great Britain with a multi-modal late fusion network (Papers Track) Abstract and authors: (click to expand)

Abstract: The ability to forecast solar photovoltaic (PV) power is important for grid balancing and reducing the CO2 intensity of electricity globally. The use of multi-modal data such as numerical weather predictions (NWPs) and satellite imagery can be harnessed to make more accurate PV forecasts. In this work, we propose a late fusion model which integrates two different NWP sources alongside satellite images to make 0-8 hour lead time forecasts for grid regions across Great Britain. We limit the model inputs to be reflective of those available in a live production system. We show how the different input data sources contribute to average error at each time horizon and compare against a simple baseline.

Authors: James Fulton (Open Climate Fix); Jacob Bieker (Open Climate Fix); Peter Dudfield (Open Climate Fix); Solomon Cotton (Open Climate Fix); Zakari Watts (Open Climate Fix); Jack Kelly (Open Climate Fix)

Power & Energy Computer Vision & Remote Sensing ICLR 2024 Exploring Graph Neural Networks to Predict the Seagrasses Ecosystem State in the Italian Seas (Papers Track) Abstract and authors: (click to expand)

Abstract: Marine coastal ecosystems (MCEs) play a critical role in climate change adaptation and human well-being. However, they face global threats from environmental pressures, both related to climate change (CC) and direct human impacts. Leveraging the increasing availability of geospatial data, this study explores Graph Neural Networks (GNNs) to assess cumulative impacts arising from human and CC related pressures on the Seagrass ecosystem in the Italian seas. Unlike traditional machine learning (ML) models with which they were compared in this study, GNNs incorporate the spatial component of data through graph structures. While experimental results demonstrate a modest performance improvement in GNNs, the study is constrained by limited data availability, preventing the exploration of the temporal component and physical laws representable through graph structures. Future efforts aim to collect higher-resolution spatial and temporal data, considering expressible environmental processes, to enhance model learning.

Authors: Angelica Bianconi (University School for Advanced Studies (IUSS) Pavia & Ca’ Foscari University of Venice); Sebastiano Vascon (Ca' Foscari University of Venice & European Centre for Living Technology); Elisa Furlan (Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) & Ca' Foscari University of Venice); Andrea Critto (Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) & Ca' Foscari University of Venice)

Ecosystems & Biodiversity Oceans & Marine Systems ICLR 2024 Global High Resolution CO2 monitoring using Super Resolution (Papers Track) Abstract and authors: (click to expand)

Abstract: Monitoring Greenhouse Gases (GHGs) concentrations and emissions is essential to mitigate climate change. Thanks to the large amount of satellite data available, it is now possible to understand GHGs' behaviours at a broad scale. However, due to remote sensing devices technological limitations, the task of global high resolution (HR) monitoring remains an open problem. To avoid waiting for new missions and better data to be generated, it is therefore relevant to experiment with processing methods able to improve existing datasets. Our paper proposes to apply Super Resolution (SR), a Deep Learning (DL) approach commonly used in Computer Vision (CV), on global L3 satellite data. We produce a daily high resolution global CO2 dataset that opens the door for globally consistent point source monitoring.

Authors: Andrianirina Rakotoharisoa (Imperial College London); Rossella Arcucci (Imperial College London)

Computer Vision & Remote Sensing Earth Observation & Monitoring ICLR 2024 DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations (Papers Track) Abstract and authors: (click to expand)

Abstract: This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics. The model is trained to probabilistically forecast day-ahead precipitation. Nonetheless, it is stable for multi-month rollouts, which reveal a qualitatively realistic superposition of convectively coupled wave modes in the tropics. Cross-spectral analysis confirms successful generation of low frequency variations associated with the Madden--Julian oscillation, which regulates most subseasonal to seasonal predictability in the observed atmosphere, and convectively coupled moist Kelvin waves with approximately correct dispersion relationships. Despite secondary issues and biases, the results affirm the potential for a next generation of global diffusion models trained on increasingly sparse, and increasingly direct and differentiated observations of the world, for practical applications in subseasonal and climate prediction.

Authors: Jason Stock (Colorado State University); Jaideep Pathak (NVIDIA Corporation); Yair Cohen (NVIDIA Corporation); Mike Pritchard (NVIDIA Corporation); Piyush Garg (NVIDIA); Dale Durran (NVIDIA Corporation); Morteza Mardani (NVIDIA Corporation); Noah D Brenowitz (NVIDIA)

Climate Science & Modeling Computer Vision & Remote Sensing Generative Modeling ICLR 2024 A cautionary tale about deep learning-based climate emulators (Papers Track) Abstract and authors: (click to expand)

Abstract: Climate models are computationally too expensive for many tasks, such as, rapidly exploring future impacts of climate policies. Thus, since the 1980s scientists have been developing lightweight approximations or emulators of climate models. Recently, deep learning has been proposed for this task and most commonly been evaluated on the benchmark ClimateBenchv1.0. We implemented a linear regression-based model from the 1990s with 30K parameters, called linear pattern scaling, that is now the 'best' model on ClimateBenchv1.0 -- outperforming the incumbent 100M-parameter foundation model, ClimaX, on the spatial error of 3 out of the 4 variables. Nevertheless, climate emulation might benefit from innovations in machine learning and we analyse two aspects that need to be addressed in future emulators: First, the data complexity depends strongly on the climate variable of interest and the chosen spatiotemporal resolution. Second, current benchmarks do not sufficiently address the large impact of interannual variability in the climate system. We have published our analysis as an interactive tutorial at github.com/ygaxolotl/tags-linear-pattern-scaling.

Authors: Björn Lütjens (Massachussets Institute of Technology); Raffaele Ferrari (Massachusetts Institute of Technology); Paolo Giani (Massachusetts Institute of Technology); Dava Newman (MIT); Andre Souza (Massachusetts Institute of Technology); Duncan Watson-Parris (University of California San Diego); Noelle Selin (Massachusetts Institute of Technology)

Climate Science & Modeling Hybrid Physical Models Time-series Analysis ICLR 2024 CausalPrompt: Enhancing LLMs with Weakly Supervised Causal Reasoning for Robust Performance in Non-Language Tasks (Papers Track) Abstract and authors: (click to expand)

Abstract: In confronting the pressing issue of climate change, we introduce "CausalPrompt", an innovative prompting strategy that adapts large language models (LLMs) for classification and regression tasks through the application of weakly supervised causal reasoning. We delve into the complexities of data shifts within energy systems, often resulting from the dynamic evolution of sensor networks, leading to discrepancies between training and test data distributions or feature inconsistencies. By embedding domain-specific reasoning in the finetuning process, CausalPrompt significantly bolsters the adaptability and resilience of energy systems to these shifts. We show that CausalPrompt significantly enhances predictions in scenarios characterized by feature shifts, including electricity demand, solar power generation, and cybersecurity within energy infrastructures. This approach underlines the crucial role of CausalPrompt in enhancing the reliability and precision of predictions in energy systems amid feature shifts, highlighting its significance and potential for real-world applications in energy management and cybersecurity, contributing effectively to climate change mitigation efforts.

Authors: Tung-Wei Lin (University of California, Berkeley); Vanshaj Khattar (Virginia Tech); Yuxuan Huang (University College London); Junho Hong (University of Michigan); Ruoxi Jia (Virginia Tech); Chen-Ching Liu (Virginia Tech); Alberto L Sangiovanni-Vincentelli (University of California, Berkeley); Ming Jin (Virginia Tech)

Natural Language Processing Buildings Power & Energy ICLR 2024 Estimating the age of buildings from satellite and morphological features to create a pan-EU Digital Building Stock Model (Papers Track) Abstract and authors: (click to expand)

Abstract: The acceleration in the effects of global warming and the recent turbulences in the energy market are further highlighting the need to act quicker and smarter in terms of decisions to transition to greener energy and reduce our overall energy consumption. With buildings accounting for about 40% of the energy consumption in Europe, it is crucial to have a comprehensive understanding of the building stock and their energy-related characteristics, including their age, in order to make informed decisions for energy savings. This study introduces a novel way to approach the age estimation of buildings at scale, using a machine learning method that integrates satellite-based imagery with morphological features of buildings. The findings demonstrate the benefits of combining these data sources and underscore the importance of incorporating local data to enable accurate prediction across different cities.

Authors: Jeremias Wenzel (Universiteit Twente); Ana M. Martinez (European Commission - Joint Research Centre); Pietro Florio (European Commission - Joint Research Centre); Katarzyna Goch (Institute of Geography and Spatial Organization Polish Academy of Sciences)

Buildings Cities & Urban Planning ICLR 2024 Probabilistic electricity price forecasting through conformalized deep ensembles (Papers Track) Abstract and authors: (click to expand)

Abstract: Probabilistic electricity price forecasting (PEPF) is subject of an increasing interest, following the demand for proper prediction uncertainty quantification, to support the operation in complex power markets with increasing share of renewable generation. Distributional neural networks ensembles (DE) have been recently shown to outperform state of the art PEPF benchmarks. Still, they require reliability improvements, as fail to pass the coverage tests at various steps on the prediction horizon. In this work, we tackle this issue by extending the DE framework with the introduction of a Conformal Prediction based technique. Experiments have been conducted on multiple market regions, achieving day-ahead probabilistic forecasts with better hourly coverage.

Authors: Alessandro Brusaferri (National Research Council of Italy); Andrea Ballarino (National Research Council of Italy); Luigi Grossi (University of Parma); Fabrizio Laurini (University of Parma)

Power & Energy ICLR 2024 Interpretable Machine Learning for power systems: Establishing Confidence in SHapley Additive exPlanationS (Papers Track) Abstract and authors: (click to expand)

Abstract: Interpretable Machine Learning (IML) is expected to remove significant barriers for the application of Machine Learning (ML) algorithms in power systems. This work first seeks to showcase the benefits of SHapley Additive exPlanations (SHAP) for understanding the outcomes of ML models, which are increasingly being used to optimise power systems with increasing share of Renewable Energy (RE), to support worldwide calls for decarbonisation and climate change. To do so, we demonstrate that the Power Transfer Distribution Factors (PTDF)—a power system physics-based linear sensitivity index—can be derived from the SHAP values. To do so, we take the derivatives of SHAP values from a ML model trained to learn line-flows from generator power-injections, using a DC power-flow case in a benchmark test network. In demonstrating that SHAP values can be related back to the physics that underpin the power system, we build confidence in the explanations SHAP can offer.

Authors: Tabia Ahmad (University of Strathclyde); Robert Hamilton (Shell); Panagiotis Papadopoulos (University of Manchester); Samuel Chevalier (University of Vermont); Ilgiz Murzakhanov (Technical University of Denmark); Rahul Nellikkath (Technical University of Denmark); Jochen Bernhard Stiasny (Technical University of Denmark); Spyros Chatzivasileiadis (Technical University of Denmark)

Power & Energy Interpretable ML ICLR 2024 DETECTION OF METEOROLOGICAL VARIABLES IN A WIND FARM INFLUENCING THE EXTREME WIND SPEED BY HETEROGENEOUS GRANGER CAUSALITY (Papers Track) Abstract and authors: (click to expand)

Abstract: For an efficiently managed wind farm and wind power generation under adverse weather, knowledge of meteorological parameters influencing wind speed is of crucial importance for optimized and improved forecasts. We investigate temporal effects of wind speed related processes such as wakes within the wind farm using the Heterogeneous Graphical Granger model. The ERA5 meteorological reanalysis was used to generate wind farm power production data in Eastern Austria. We evaluated six different scenarios for the hydrological half-year period, based on moderate wind speed and varying temporal intervals of low or high extreme wind speed This allows to carry out causal reasoning about possible causes of extreme wind speed in a wind farm. A set of causal parameters for each of the scenarios was discovered enabling future early warning and for taking management measures for wind farm power generation management under adverse weather conditions.

Authors: Katerina Schindlerova (UniVie); Irene Schicker (Geos); Kejsi Hoxhallari (UniVie); Claudia Plant (University of Vienna, Austria)

Causal & Bayesian Methods Extreme Weather ICLR 2024 Machine Learning for the Detection of Arctic Melt Ponds from Infrared Imagery (Papers Track) Abstract and authors: (click to expand)

Abstract: Melt ponds are pools of water on Arctic summer sea ice that play an important role in the Arctic climate system. Retrieving their coverage is essential to better understand and predict the rapidly changing Arctic, but current data are limited. The goal of this study is to enhance melt pond data by developing a method that segments thermal infrared (TIR) airborne imagery into melt pond, sea ice, and ocean classes. Due to temporally and spatially varying surface temperatures, we use a data-driven deep learning approach to solve this task. We adapt and fine-tune AutoSAM, a Segment Anything-based segmentation model. We make the code, data, and models available online.

Authors: Marlena Reil (University of Osnabrück and University of Bremen, Institute of Environmental Physics); Gunnar Spreen (University of Bremen, Institute of Environmental Physics); Marcus Huntemann (University of Bremen, Institute of Environmental Physics); Lena Buth (Alfred Wegener Institute); Dennis Wilson (University of Toulouse, ISAE-Supaero)

Earth Observation & Monitoring Computer Vision & Remote Sensing ICLR 2024 Identifying Complex Dynamics of Power Grid Frequency (Papers Track) Abstract and authors: (click to expand)

Abstract: The energy system is undergoing rapid changes to integrate a growing number of intermittent renewable generators and facilitate the broader transition toward sustainability. As millions of consumers and thousands of (volatile) generators are connected to the same synchronous grid, no straightforward bottom-up models describing the dynamics are available on a continental scale comprising all of these necessary details. Hence, to identify this unknown power grid dynamics, we propose to leverage the Sparse Identification of Nonlinear Dynamics (SINDy) method. Thereby, we unveil the governing equations underlying the dynamical system directly from data measurements. Investigating the power grids of Iceland, Ireland and the Balearic islands as sample systems, we observe structurally similar dynamics with remarkable differences in both quantitative and qualitative behavior. Overall, we demonstrate how complex, i.e. non-linear, noisy, and time-dependent, dynamics can be identified straightforwardly.

Authors: Xinyi Wen (Karlsruhe Institute of Technology); Ulrich Oberhofer (Karlsruhe Institute of Technology); Leonardo Rydin Gorjão (Norwegian University of Life Sciences); G.Cigdem YALCIN (Istanbul University); Veit Hagenmeyer (Karlsruhe Institute of Technology (KIT)); Benjamin Schäfer (Karlsruhe Institute of Technology)

Power & Energy Time-series Analysis ICLR 2024 Imbalance-aware Presence-only Loss Function for Species Distribution Modeling (Papers Track) Abstract and authors: (click to expand)

Abstract: In the face of significant biodiversity decline, species distribution models (SDMs) are essential for understanding the impact of climate change on species habitats by connecting environmental conditions to species occurrences. Traditionally limited by a scarcity of species observations, these models have significantly improved in performance through the integration of larger datasets provided by citizen science initiatives. However, they still suffer from the strong class imbalance between species within these datasets, often resulting in the penalization of rare species--those most critical for conservation efforts. To tackle this issue, this study assesses the effectiveness of training deep learning models using a balanced presence-only loss function on various large citizen science-based datasets. We demonstrate that this imbalance-aware loss function outperforms traditional loss functions across various datasets and tasks, particularly in accurately modeling rare species with limited observations.

Authors: Robin Zbinden (EPFL); Nina van Tiel (EPFL); Marc Rußwurm (Wageningen University); Devis Tuia (EPFL)

Ecosystems & Biodiversity ICLR 2024 Deep Gaussian Processes and inversion for decision support in model-based climate change mitigation and adaptation problems (Papers Track) Abstract and authors: (click to expand)

Abstract: To inform their decisions, policy makers often rely on models developed by researchers that are computationally intensive and complex and that frequently run on High Performance Computers (HPC). These decision-support models are not used directly by deciders and the results of these models tend to be presented by experts as a limited number of potential scenarios that would result from a limited number of potential policy choices. Machine learning models such as Deep Gaussian Processes (DGPs) can be used to radically re-define how decision makers can use models by creating a ‘surrogate model’ or ‘emulator’ of the original model. Surrogate models can then be embedded into apps that decisions makers can use to directly explore a vast array of policy options corresponding to potential target outcomes (model inversion). To illustrate the mechanism, we give an example of application that is envisaged as part of the UK government’s Net Zero strategy. To achieve Net Zero CO2 emissions by 2050, the UK government is considering multiple options that include planting trees to capture carbon. However, the amount of CO2 captured by the trees depend on a large number of factors that include climate conditions, soil type, soil carbon, tree type, ... Depending on these factors the net balance of carbon removal after planting trees may not necessarily be positive. Hence, choosing the right place to plant the right tree is very important. A decision-helping model has been developed to tackle this problem. For a given policy input, the model outputs its impact in terms of CO2 sequestration, biodiversity and other ecosystem services. We show how DGPs can be used to create a surrogate model of this original afforestation model and how these can be embedded into an R shiny app that can then be directly used by decision makers.

Authors: bertrand nortier (University of Exeter); daniel williamson (University of Exeter); mattia mancini (University of Exeter); amy binner (University of Exeter); brett day (University of Exeter); ian bateman (University of Exeter)

Public Policy Carbon Capture & Sequestration Ecosystems & Biodiversity Forests Causal & Bayesian Methods Uncertainty Quantification & Robustness ICLR 2024 Generalized Policy Learning for Smart Grids: FL TRPO Approach (Papers Track) Abstract and authors: (click to expand)

Abstract: The smart grid domain requires bolstering the capabilities of existing energy management systems; Federated Learning (FL) aligns with this goal as it demonstrates a remarkable ability to train models on heterogeneous datasets while maintaining data privacy, making it suitable for smart grid applications, which often involve disparate data distributions and interdependencies among features that hinder the suitability of linear models. This paper introduces a framework that combines FL with a Trust Region Policy Optimization (FL TRPO) aiming to reduce energy-associated emissions and costs. Our approach reveals latent interconnections and employs personalized encoding methods to capture unique insights, understanding the relationships between features and optimal strategies, allowing our model to generalize to previously unseen data. Experimental results validate the robustness of our approach, affirming its proficiency in effectively learning policy models for smart grid challenges.

Authors: Yunxiang LI (MBZUAI); Nicolas M Cuadrado (MBZUAI); Samuel Horváth (MBZUAI); Martin Takac (Mohamed bin Zayed University of Artificial Intelligence)

Power & Energy Reinforcement Learning ICLR 2024 A Deep Learning Framework to Efficiently Estimate Precipitation at the Convection Permitting Scale (Papers Track) Abstract and authors: (click to expand)

Abstract: Precipitation-related extreme events are rapidly growing due to climate change, emphasizing the need for accurate hazard projections. To effectively model the convective phenomena driving severe precipitation, high-resolution estimates are crucial. Existing methods struggle with either insufficient expressiveness in capturing complex convective dynamics, due to the low resolution, or excessive computational demands. In response, we propose an innovative deep learning framework that efficiently harnesses available data to yield precise results. This model, based on graph neural networks, utilises two grids with different resolution and two sets of edges to represent spatial relationships. Employing as input ERA5 reanalysis atmospheric variables on an approximately 25 km grid, the framework produces hourly precipitation estimates on a finer 3 km grid. Findings are promising in accurately capturing yearly precipitation distribution and estimating cumulative precipitation during extreme events. Notably, the model demonstrates effectiveness in spatial regions not included in the training, motivating further exploration of its transferability potential.

Authors: Valentina Blasone (University of Trieste); Erika Coppola (Earth System Physics Section, ICTP, Trieste); Guido Sanguinetti (SISSA); Viplove Arora (Theoretical and Scientific Data Science, SISSA, Trieste); Serafina Di Gioia (Earth System Physics Section, ICTP, Trieste); Luca Bortolussi (University of Trieste)

Climate Science & Modeling Extreme Weather ICLR 2024 Global Vegetation Modeling With Pre-Trained Weather Transformers (Papers Track) Abstract and authors: (click to expand)

Abstract: Accurate vegetation models can produce further insights into the complex inter-action between vegetation activity and ecosystem processes. Previous research has established that long-term trends and short-term variability of temperature and precipitation affect vegetation activity. Motivated by the recent success of Transformer-based Deep Learning models for medium-range weather forecasting, we adapt the publicly available pre-trained FourCastNet to model vegetation activity while accounting for the short-term dynamics of climate variability. We investigate how the learned global representation of the atmosphere’s state can be transferred to model the normalized difference vegetation index (NDVI). Our model globally estimates vegetation activity at a resolution of 0.25◦ while relying only on meteorological data. We demonstrate that leveraging pre-trained weather models improves the NDVI estimates compared to learning an NDVI model from scratch. Additionally, we compare our results to other recent data-driven NDVI modeling approaches from machine learning and ecology literature. We further provide experimental evidence on how much data and training time is necessary to turn FourCastNet into an effective vegetation model. Code and models are available at https://github.com/LSX-UniWue/Global-Ecosystem-Modeling.

Authors: Pascal Janetzky (University Wuerzburg); Florian Gallusser (Universität Würzburg); Simon Hentschel (Julius-Maximilians-Universität of Würzburg); Andreas Hotho (University of Wuerzburg); Anna Krause (Universität Würzburg, Department of Computer Science, CHair X Data Science)

Ecosystems & Biodiversity Climate Science & Modeling Data Mining ICLR 2024 Building Ocean Climate Emulators (Papers Track) Abstract and authors: (click to expand)

Abstract: The current explosion in machine learning for climate has led to skilled, computationally cheap emulators for the atmosphere. However, the research for ocean emulators remains nascent despite the large potential for accelerating coupled climate simulations and improving ocean forecasts on all timescales. There are several fundamental questions to address that can facilitate the creation of ocean emulators. Here we focus on two questions: 1) the role of the atmosphere in improving the extended skill of the emulator and 2) the representation of variables with distinct timescales (e.g., velocity and temperature) in the design of any emulator. In tackling these questions, we show stable prediction of surface fields for over 8 years, training and testing on data from a high-resolution coupled climate model, using results from four regions of the globe. Our work lays out a set of physically motivated guidelines for building ocean climate emulators.

Authors: Adam Subel (New York University); Laure Zanna (New York University)

Climate Science & Modeling ICLR 2024 Neural Tree Reconstruction for the Open Forest Observatory (Papers Track) Abstract and authors: (click to expand)

Abstract: The Open Forest Observatory (OFO) is a collaboration across universities and other partners to make low-cost forest mapping accessible to ecologists, land managers, and the general public. The OFO is building both a database of geospatial forest data as well as open-source methods and tools for forest mapping by un- crewed aerial vehicle. Such data are useful for a variety of climate applications including prioritizing reforestation efforts, informing wildfire hazard reduction, and monitoring carbon sequestration. In the current iteration of the OFO’s forest map database, 3D tree maps are created using classical structure-from-motion techniques. This approach is prone to artifacts, lacks detail, and has particular difficulty on the forest floor where the input data (overhead imagery) has limited visibility. These reconstruction errors can potentially propagate to the down- stream scientific tasks (e.g. a wildfire simulation.) Advances in 3D reconstruction, including methods like Neural Radiance Fields (NeRF), produce higher quality results that are more robust to sparse views and support data-driven priors. We explore ways to incorporate NeRFs into the OFO dataset, outline future work to support even more state-of-the-art 3D vision models, and describe the importance of high-quality 3D reconstructions for forestry applications.

Authors: Marissa Ramirez de Chanlatte (UC Berkeley); Arjun Rewari (Darrell Group, Berkeley AI Research Lab); Trevor Darrell (UC Berkeley); Derek Young (University of California Davis)

Forests Carbon Capture & Sequestration ICLR 2024 Graph Neural Network Based Screening of Metal-Organic Frameworks for CO2 Capture (Papers Track) Abstract and authors: (click to expand)

Abstract: Our ability to capture and remove carbon dioxide (CO2) at gigaton scale within a decade or two depends on our ability to quickly identify new materials that are high performing, selective over other gases with low energy demand and then further develop them for large scale deployment. As a proven technology for gas separation in other industrial applications, metal-organic frameworks (MOF) come in virtually unlimited number of crystal combinations in their highly porous lattice and may offer the solution for CO2 capture from atmosphere or industrial point sources. Although MOFs can have highly complex crystal structure, which cannot be easily exploited in tabular data format in conventional ML methods or more recent Deep Learning methods, Graph Neural Networks can easily be trained on their representative crystallographic information file (CIF) content. In this work, we train GNNs to create an end-to-end workflow to screen large number of MOF crystal structures directly from the data within the crystallographic information files for their CO2 working capacity or CO2/N2 selectivity under low-pressure conditions. Our preliminary results show that a simple 2-layered Graph Convolution Networks (GCN) can easily achieve R2 score in the range of 0.87 to 0.89, easily.

Authors: Zikri Bayraktar (Schlumberger Doll Research); Mengying Li (Schlumberger Doll Research); Shahnawaz Molla (Schlumberger Doll Research)

Carbon Capture & Sequestration ICLR 2024 Predicting Species Occurrence Patterns from Partial Observations (Papers Track) Abstract and authors: (click to expand)

Abstract: To address the interlinked biodiversity and climate crises, we need an understanding of where species occur and how these patterns are changing. However, observational data on most species remains very limited, and the amount of data available varies greatly between taxonomic groups. We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies, which is designed to pair with the existing SatBird dataset of bird observational data. To address this task, we propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found. We find that R-Tran outperforms other methods in predicting species encounter rates with partial information both within a taxon (birds) and across taxa (birds and butterflies). Our approach opens new perspectives to leveraging insights from species with abundant data to other species with scarce data, by modelling the ecosystems in which they co-occur.

Authors: Hager Radi Abdelwahed (Mila: Quebec AI Institute); Mélisande Teng (Mila, Université de Montréal); David Rolnick (MIT)

Ecosystems & Biodiversity Computer Vision & Remote Sensing ICLR 2024 Towards Scalable Deep Species Distribution Modelling using Global Remote Sensing (Papers Track) Abstract and authors: (click to expand)

Abstract: Destruction of natural habitats and anthropogenic climate change are threatening biodiversity globally. Addressing this loss necessitates enhanced monitoring techniques to assess the impa