Wildfires present intricate challenges for prediction, necessitating the use of sophisticated machine learning techniques for effective modeling\cite{jain2020review}. In our research, we conducted a thorough assessment of various machine learning algorithms for both classification and regression tasks relevant to predicting wildfires. We found that for classifying different types or stages of wildfires, the XGBoost model outperformed others in terms of accuracy and robustness. Meanwhile, the Random Forest regression model showed superior results in predicting the extent of wildfire-affected areas, excelling in both prediction error and explained variance. Additionally, we developed a hybrid neural network model that integrates numerical data and image information for simultaneous classification and regression. To gain deeper insights into the decision-making processes of these models and identify key contributing features, we utilized eXplainable Artificial Intelligence (XAI) techniques, including TreeSHAP, LIME, Partial Dependence Plots (PDP), and Gradient-weighted Class Activation Mapping (Grad-CAM). These interpretability tools shed light on the significance and interplay of various features, highlighting the complex factors influencing wildfire predictions. Our study not only demonstrates the effectiveness of specific machine learning models in wildfire-related tasks but also underscores the critical role of model transparency and interpretability in environmental science applications.
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
Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements
Institute of Software Engineering University of Stuttgart Stuttgart, Germany Email；Institute of Software Engineering University of Stuttgart Stuttgart, Germany
Explainable AI for Engineering Design: A Unified Approach of Systems Engineering and Component- Based Deep Learning Demonstrated by Energy- Efficient Building Design
Leibniz University Hannover（汉诺威莱布尼茨大学）；Technical University of Munich（慕尼黑工业大学）
Explainable Artificial Intelligence Model for Evaluating Shear Strength Parameters of Municipal Solid Waste Across Diverse Compositional Profiles
The Joint Graduate School of Energy and Environment, King Mongkut's University of Technology Thonburi（泰国国王科技大学吞武里校区能源与环境联合研究生院）；AI Research Group, Department of Civil Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi（泰国国王科技大学吞武里校区工程学院土木工程系人工智能研究组）；Center of Excellence on Energy Technology and Environment (CEE), PERDO, Ministry of Higher Education, Science, Research and Innovation (MHESI)（高等教育、科学、研究与创新部PERDO能源技术与环境卓越中心）；Department of Environmental Engineering, Faculty of Engineering, Kasetsart University（农业大学工程学院环境工程系）
Explainable Quantum Machine Learning for Multispectral Images Segmentation: Case Study
Systems Research Institute Polish Academy of Sciences（波兰科学院系统研究所）；Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences（波兰科学院尼古拉·哥白尼天文中心）；Center of Excellence in Artificial Intelligence, AGH University（AGH大学人工智能卓越中心）
State-of-Health Prediction for EV Lithium-Ion Batteries via DLinear and Robust Explainable Feature Selection
Department of Chemical Engineering（化学工程系）；Massachusetts Institute of Technology（麻省理工学院）；Department of Chemical and Biomolecular Engineering（化学与生物分子工程系）；Yonsei University（延世大学）；Department of Battery Engineering（电池工程系）
Explainable AI for Comparative Analysis of Intrusion Detection Models
Embry-Riddle Aeronautical University（安柏瑞德航空大学）；University of Tennessee at Martin（田纳西大学马丁分校）；Bowling Green State University（鲍灵格林州立大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩郡分校）
Explainable Dual-Attention Tabular Transformer for Soil Electrical Resistivity Prediction: A Decision Support Framework for High-Voltage Substation Construction
King Mongkut’s University of technology Thonburi（泰国国王科技大学）
本研究提出一种用于预测土壤电阻率的双注意力 Transformer 架构，结合粒子群优化调参，在保持高预测精度的同时通过 SHAP 值提供可解释性，并开发了辅助高压变电站建设的 Web 决策支持框架。
Engineering Spatial and Molecular Features from Cellular Niches to Inform Predictions of Inflammatory Bowel Disease
Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida（电气与计算机工程系，赫伯特·韦斯剧院工程学院，佛罗里达大学）；Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida（流行病学系，公共卫生与健康专业学院及医学院，佛罗里达大学）；Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida（药理学与转化研究系，药学院，佛罗里达大学）
Optimizing Stroke Risk Prediction: A Machine Learning Pipeline Combining ROS-Balanced Ensembles and XAI
1 Department of Robotics, Robo Tech Valley, Dhaka, Bangladesh 2 Department of Computer Science；Engineering, Jagannath University, Dhaka, Bangladesh 3 Department of Internet of Things \& Robotics Engineering, University of Frontier Technology, Bangladesh
