Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
Clemson University（克莱姆森大学）；NSF National Center for Atmospheric Research（美国国家科学基金会国家大气研究中心）；University of San Francisco（旧金山大学）；Northern Arizona University（北亚利桑那大学）；U.S.D.A. Forest Service, Pacific Wildland Fire Science Laboratory（美国农业部林务局太平洋野火科学实验室）；Georgia Institute of Technology（佐治亚理工学院）
Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with Graph Neural Network Communication Layer for Open-ended Wildfire-Management Resource Distribution
Efficient first-order algorithms for large-scale, non-smooth maximum entropy models with application to wildfire science
Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）；Department of Earth and Environmental Engineering, Columbia University（哥伦比亚大学地球与环境工程系）；Division of Applied Mathematics, Brown University（布朗大学应用数学系）
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）
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）
Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data
Spatial Informatics Group, LLC.（空间信息集团有限责任公司）；ADNET systems, INC.（ADNET系统公司）；NASA Langley Research Center（美国国家航空航天局兰利研究中心）；University at Albany（奥尔巴尼大学）
Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management
National Center for Atmospheric Research (NCAR)（美国国家大气研究中心）；Cooperative Institute for Research in Environmental Sciences (CIRES)（环境科学合作研究所）；University of Colorado（科罗拉多大学）；Global Systems Laboratory, National Oceanic and Atmospheric Administration (NOAA)（美国国家海洋和大气管理局全球系统实验室）
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）
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%。
Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Brookhaven National Laboratory（布鲁克海文国家实验室）；Oak Ridge National Laboratory（橡树岭国家实验室）；University of Pittsburgh（匹兹堡大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Massachusetts（马萨诸塞大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the FIREX-AQ Datasets
Spatial Informatics Group, LLC.（空间信息集团有限公司）；Jet Propulsion Laboratory, California Institute of Technology（喷气推进实验室，加州理工学院）；University of California at Berkeley（加州大学伯克利分校）；Fowler School of Engineering, Chapman University（查普曼大学福勒工程学院）；Machine Learning and Assistive Technology Lab (MLAT), Chapman University（机器学习与辅助技术实验室（MLAT），查普曼大学）
A survey study of success factors in data science projects
Vicomtech Foundation Basque Research and Technology Alliance（Vicomtech基金会巴斯克研究与技术联盟）；University of Navarra TECNUN School of Engineering（纳瓦拉大学TECNUN工程学院）
