National Observatory of Athens（雅典国家天文台）；Universitat de València（瓦伦西亚大学）；Harokopio University of Athens（雅典哈罗科皮奥大学）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）
Ioannis Prapas, Akanksha Ahuja, Spyros Kondylatos, Ilektra Karasante, Eleanna Panagiotou, Lazaro Alonso, Charalampos Davalas, Dimitrios Michail, Nuno Carvalhais, Ioannis Papoutsis
备注：
Accepted at the NeurIPS 2022 workshop on Tackling Climate Change with Machine Learning. Version 2 has corrected the table of results (Table 1)
Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In this work, we create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale with the use of segmentation deep learning models. Particularly, we present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers (climate, vegetation, oceanic indices, human-related variables), as well as the historical burned areas and wildfire emissions for 2001-2021. We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time. Our work motivates the use of deep learning for global burned area forecasting and paves the way towards improved anticipation of global wildfire patterns.
National Observatory of Athens（雅典国家天文台）；Universitat de València（瓦伦西亚大学）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）
Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability. We approach daily fire danger prediction as a machine learning task, using historical Earth observation data from the last decade to predict next-day's fire danger. To that end, we collect, pre-process and harmonize an open-access datacube, featuring a set of covariates that jointly affect the fire occurrence and spread, such as weather conditions, satellite-derived products, topography features and variables related to human activity. We implement a variety of Deep Learning (DL) model…
Spatio-Temporal Wildfire Spread Prediction in Canada using a Video Swin-Hybrid-U-Net and Satellite Imagery
Faculty of Science, University of Alberta（阿尔伯塔大学理学院）；Interdisciplinary Lab for Mathematical Ecology & Epidemiology (ILMEE), University of Alberta（阿尔伯塔大学数学生态与流行病学跨学科实验室）；Department of Mathematical and Statistical Sciences, University of Alberta（阿尔伯塔大学数学与统计科学系）
Set Prediction for Next-Day Active Fire Forecasting
Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Orion Lab（奥里昂实验室）；University of Utah（犹他大学）；National Observatory of Athens（雅典国家天文台）；Earth System Science Program, School of Sciences and Engineering, Universidad del Rosario（地球系统科学计划，科学与工程学院，罗萨里奥大学）；Departamento de Ciências e Engenharia do Ambiente, Faculdade de Ciências e Tecnologia, Universidade Nova Lisboa（环境科学与工程系，科学与技术学院，新里斯本大学）；ELLIS Unit Jena（耶纳ELLIS单位）
Deep Learning for Wildfire Risk Prediction: Integrating Remote Sensing and Environmental Data
Schulich School of Engineering, Department of Geomatics Engineering, University of Calgary（Schulich 工程学院，测绘工程系，卡尔加里大学）；Department of Geography and Environmental Management, University of Waterloo（地理与环境管理系，滑铁卢大学）；Department of Systems Design Engineering, University of Waterloo（系统设计工程系，滑铁卢大学）；School of Emergency Management, Nanjing University of Information Science and Technology（应急管理学院，南京信息工程大学）；Division of Geoinformatics, KTH Royal Institute of Technology（地理信息学系，皇家理工学院）；School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（遥感与测绘工程学院，南京信息工程大学）；Department of Software Engineering, University of Waterloo（软件工程系，滑铁卢大学）；Department of Management Science and Engineering, University of Waterloo（管理科学与工程系，滑铁卢大学）
Wildfires are uncontrolled fires in the environment that can be caused by humans or nature. In 2020 alone, wildfires in California have burned 4.2 million acres, damaged 10,500 buildings or structures, and killed more than 31 people, exacerbated by climate change and a rise in average global temperatures. This also means there has been an increase in the costs of extinguishing these treacherous wildfires. The objective of the research is to detect forest fires in their earlier stages to prevent them from spreading, prevent them from causing damage to a variety of things, and most importantly, …
Accurate forecasts of fine particulate matter (PM 2.5) from wildfire smoke are crucial to safeguarding cardiopulmonary public health. Existing forecasting systems are trained on sparse and inaccurate ground truths, and do not take sufficient advantage of important spatial inductive biases. In this work, we present a convolutional neural network which preserves sparsity invariance throughout, and leverages multitask learning to perform dense forecasts of PM 2.5values. We demonstrate that our model outperforms two existing smoke forecasting systems during the 2018 and 2019 wildfire season in Bri…
California Wildfire Inventory (CAWFI): An Extensive Dataset for Predictive Techniques based on Artificial Intelligence
Stanford University（斯坦福大学）；Harvard University T. H. Chan School of Public Health（哈佛大学T.H. Chan公共卫生学院）；University of Texas Health Science Center（德克萨斯大学健康科学中心）
Stanford University（斯坦福大学）；University of Cambridge（剑桥大学）；ENGIE Lab CRIGEN（ENGIE集团CRIGEN实验室）；Dubai Energy and Water Authority（迪拜水电局）
Solar forecasting from ground-based sky images has shown great promise in reducing the uncertainty in solar power generation. With more and more sky image datasets open sourced in recent years, the development of accurate and reliable deep learning-based solar forecasting methods has seen a huge growth in potential. In this study, we explore three different training strategies for solar forecasting models by leveraging three heterogeneous datasets collected globally with different climate patterns. Specifically, we compare the performance of local models trained individually based on single da…
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques. In most cases, GFMs are implemented using deep neural networks, and in particular Recurrent Neural Networks (RNN), which require a sufficient amount of time series to estimate their numerous model parameters. However, many time series databases have only a limited number of time series. In this study, we propose a novel, …
University of Oxford（牛津大学）；Inria（法国国家信息与自动化研究所）；University College London（伦敦大学学院）；University of Warsaw（华沙大学）
Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts, to mitigate against such events. However, there is currently no benchmark dataset dedicated to the study of global precipitation forecasts. In this paper, we introduce \textbf{RainBench}, a new multi-modal benchmark dataset for data-driven precipitation forecasting. It includes simulated satellite data, a selection …
EWMoE: An effective model for global weather forecasting with mixture-of-experts
University of Electronic Science and Technology of China（电子科技大学）；Sichuan Artificial Intelligence Research Institute（四川人工智能研究院）；Institute of Plateau Meteorology, China Meteorological Administration（中国气象局高原气象研究所）
Advanced Global Wildfire Activity Modeling with Hierarchical Graph ODE
University of Science and Technology of China（中国科学技术大学）；Tsinghua University（清华大学）；Southwest Jiaotong University（西南交通大学）；Beijing Jiaotong University（北京交通大学）；Tencent（腾讯）；Nanyang Technological University（南洋理工大学）
Fire-EnSF: Wildfire Spread Data Assimilation using Ensemble Score Filter
H. Milton Stewart School of Industrial and Systems Engineering（H. Milton Stewart工业与系统工程学院）；School of Earth and Atmospheric Sciences（地球与大气科学学院）；Georgia Institute of Technology（佐治亚理工学院）
Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts
University of Southern California（南加州大学）；University of Maryland（马里兰大学）；San Jose State University（圣何塞州立大学）；University of Colorado Denver（科罗拉多大学丹佛分校）；Colorado State University（科罗拉多州立大学）；University of Utah（犹他大学）
Over the past decade, the number of wildfire has increased significantly around the world, especially in the State of California. The high-level concentration of greenhouse gas (GHG) emitted by wildfires aggravates global warming that further increases the risk of more fires. Therefore, an accurate prediction of wildfire occurrence greatly helps in preventing large-scale and long-lasting wildfires and reducing the consequent GHG emissions. Various methods have been explored for wildfire risk prediction. However, the complex correlations among a lot of natural and human factors and wildfire ign…
Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling
Mohamed bin Zayed University of Artificial Intelligence（莫德尔·本·扎耶德人工智能大学）；Weizmann Institute of Science（魏兹曼科学研究所）；Linköping University（林雪平大学）；Australian National University（澳大利亚国立大学）
Operational evaluation of data-driven forest fire forecasting models
CMCC Foundation - Euro-Mediterranean Center on Climate Change, Italy（CMCC基金会 - 欧洲-地中海气候变化中心，意大利）；Department of Engineering for Innovation, University of Salento, Italy（萨伦托大学创新工程系，意大利）
From Black Box to Insight: Explainable AI for Extreme Event Preparedness
Department of Cybersecurity University at Albany, SUNY Albany, NY, USA；Dept. of Atmospheric \& Environmental Sciences University at Albany, SUNY Albany, NY, USA；Lally School of Management Rensselaer Polytechnic Institute Albany, NY, USA；Goddard Space Flight Center NASA Greenbelt, MD, USA
