Wildfire prediction has become increasingly crucial due to the escalating impacts of climate change. Traditional CNN-based wildfire prediction models struggle with handling missing oceanic data and addressing the long-range dependencies across distant regions in meteorological data. In this paper, we introduce an innovative Graph Neural Network (GNN)-based model for global wildfire prediction. We propose a hybrid model that combines the spatial prowess of Graph Convolutional Networks (GCNs) with the temporal depth of Long Short-Term Memory (LSTM) networks. Our approach uniquely transforms global climate and wildfire data into a graph representation, addressing challenges such as null oceanic data locations and long-range dependencies inherent in traditional models. Benchmarking against established architectures using an unseen ensemble of JULES-INFERNO simulations, our model demonstrates superior predictive accuracy. Furthermore, we emphasise the model's explainability, unveiling potential wildfire correlation clusters through community detection and elucidating feature importance via Integrated Gradient analysis. Our findings not only advance the methodological domain of wildfire prediction but also underscore the importance of model transparency, offering valuable insights for stakeholders in wildfire management.
Thermodynamic-Inspired Explainable GeoAI: Uncovering Regime-Dependent Mechanisms in Heterogeneous Spatial Systems
Geoinformation and Big Data Research Lab, Department of Geography, Pennsylvania State University（宾夕法尼亚州立大学地理系地理信息与大数据研究实验室）；Department of Materials Science and Engineering, Pennsylvania State University（宾夕法尼亚州立大学材料科学与工程系）
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）
Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
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
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）
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）
VARSHAP: Addressing Global Dependency Problems in Explainable AI with Variance-Based Local Feature Attribution
Faculty of Computing and Telecommunications（计算机与电信学院）；Poznan University of Technology（波兹南技术大学）；Faculty of Mathematics, Informatics and Mechanics（数学、信息学与力学学院）；University of Warsaw（华沙大学）；MIM Solutuions Research Institute（MIM解决方案研究所）
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）
Explainable Uncertainty Quantification for Wastewater Treatment Energy Prediction via Interval Type-2 Neuro-Fuzzy System
Jheronimus Academy of Data Science, Eindhoven University of Technology, Eindhoven, The Netherlands（杰罗尼穆斯数据科学学院，埃因霍温理工大学，埃因霍温，荷兰）；Haskoning, Amersfoort, The Netherlands（哈索宁，阿默斯福尔特，荷兰）；School of Industrial Engineering, Eindhoven University of Technology（工业工程学院，埃因霍温理工大学）
Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds
University of Cambridge（剑桥大学）；University of Valencia（瓦伦西亚大学）；Institut Pierre-Simon Laplace（皮埃尔-西蒙·拉普拉斯研究所）；Columbia University（哥伦比亚大学）；University of Oxford（牛津大学）；Pasteur Labs（巴斯德实验室）；ISI
Federated Causality Learning with Explainable Adaptive Optimization
School of Software, Shandong University（山东大学软件学院）；SDU-NTU Joint Centre for AI Research, Shandong University（山东大学-新加坡南洋理工大学人工智能联合研究中心）；Department of Mathematics, National University of Singapore（新加坡国立大学数学系）；Departiment of Computer Science, George Mason University（乔治梅森大学计算机系）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）
针对隐私保护下分散异构数据的因果发现问题，提出 FedCausal 联邦策略，通过可解释的自适应优化统一局部与全局 DAG 学习，在 non-iid 合成及真实数据上表现优越。
Explainable Student Performance Prediction With Personalized Attention for Explaining Why A Student Fails
School of Computer Science (National Pilot Software Engineering School)（计算机科学学院（国家示范性软件工程学院））；Beijing University of Posts and Telecommunications（北京邮电大学）
Nanjing University（南京大学）；School of Intelligent Software and Engineering, Nanjing University (Suzhou Campus)（南京大学（苏州校区）智能软件与工程学院）；Hunan Normal University（湖南师范大学）；Hunan University of Technology and Business（湖南工商大学）；Brunel University London（布鲁内尔伦敦大学）
