Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. Many efforts have been made to understand the causes of the increases in wildfire intensity and frequency in recent years. In this work, we propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California by utilizing a multitude of environmental data including population density, Normalized Difference Vegetation Index (NDVI), Palmer Drought Severity Index (PDSI), tree mortality area, tree mortality number, and altitude. Moreover, we focus on a better understanding of the impacts of different factors so as to inform preventive actions. To validate our models and findings, we divide the land of California into 4,242 grids of 0.1 degrees $\times$ 0.1 degrees in latitude and longitude, and compute the risk of each grid based on spatial and temporal conditions. To verify the generalizability of our models, we further expand the scope of wildfire risk assessment from California to Washington without any fine tuning. By performing counterfactual analysis, we uncover the effects of several possible methods on reducing the number of high risk wildfires. Taken together, our study has the potential to estimate, monitor, and reduce the risks of wildfires across diverse areas provided that such environment data is available.
Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
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）
Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5
College of Engineering, University of Georgia（佐治亚大学工程学院）；Information System Department, UMBC（马里兰大学巴尔的摩郡分校信息系统系）；College of Graduate and Professional Studies, Trine University（特瑞大学研究生与专业研究学院）；School of Professional Studies, Columbia University（哥伦比亚大学专业研究学院）
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）
GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface
Urban Reslience.AI Lab, Zachry Department of Civil and Environmental Engineering, Texas A&M University（Urban Reslience.AI实验室，Zachry土木与环境工程系，德克萨斯A&M大学）；Department of Computer Science and Engineering, Texas A&M University（计算机科学与工程系，德克萨斯A&M大学）
A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model
CEREA, ENPC, EDF R&D, Institut Polytechnique de Paris（CEREA、ENPC、EDF R&D、巴黎理工学院）；Department of Earth Science & Engineering, Imperial College London（帝国理工学院伦敦地球科学与工程系）；Department of Computing, Imperial College London（帝国理工学院伦敦计算机科学系）；Data Science Institute, Imperial College London（帝国理工学院伦敦数据科学研究院）
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）
Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data
Imperial College London（帝国理工学院）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；École des Ponts（巴黎路桥学院）；EDF R&D（法国电力研发中心）；Institut Polytechnique de Paris（巴黎综合理工学院）；University of California, Davis（加利福尼亚大学戴维斯分校）；Hong Kong University of Science and Technology（香港科技大学）；University of Reading（雷丁大学）
Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks
School of Mathematics, University of Edinburgh（爱丁堡大学数学学院）；Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学计算机、电气与数学科学与工程（CEMSE）分部统计项目）
Tensor Network Machine Learning for Wildfire Susceptibility Mapping: from Grokking Dynamics to Quantum Mixedness of Class Representations
Dipartimento Interuniversitario di Fisica, Università degli Studi di Bari Aldo Moro（巴里阿尔多·莫罗大学物理跨大学部门）；Istituto Nazionale di Fisica Nucleare, Sezione di Bari（巴里国家核物理研究所）；Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro（巴里阿尔多·莫罗大学生物医学转化与神经科学部门）；Dipartimento Di Scienze Del Suolo, Della Pianta e Degli Alimenti (DISSPA), Università degli Studi di Bari Aldo Moro（巴里阿尔多·莫罗大学土壤、植物和食品科学部门）
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）
FUELVISION: A Multimodal Data Fusion and Multimodel Ensemble Algorithm for Wildfire Fuels Mapping
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Desert Research Institute（沙漠研究所）；Amazon（亚马逊公司）；United States Forest Service（美国林务局）
本文提出 FUELVISION 算法，融合多源遥感与地形数据，结合生成式 AI 合成数据增强训练集，采用多模型集成方法实现近实时野火可燃物制图，准确率近 80%。
CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia
Department of Computer Engineering（计算机工程系）；Dwarkadas Jivanlal Sanghvi College of Engineering（德瓦尔卡斯·吉文拉尔·桑格维学院）；College of Computing（计算机学院）；Georgia Institute of Technology（佐治亚理工学院）；Geo-Hydro Meteorological Risks Management Division（地质-水文气象灾害管理部）；National Institute of Disaster Management（国家灾害管理研究所）
