Machine Learning for Climate Hazard Modeling: A Comprehensive Review of Methods, Applications, and Challenges

Abstract

The increasing frequency and severity of climate-related hazards have intensified the demand for reliable approaches to their analysis, prediction, and risk management. In recent years, machine learning (ML) has emerged as a powerful data-driven paradigm with broad applications in climate hazard modeling. This review provides a comprehensive synthesis and critical assessment of recent studies investigating the application of machine learning techniques to the modeling of major climate-related hazards, including floods, droughts, heatwaves, Wildfire, storms and other climate change–induced extreme events. Previous research employing algorithms such as Random Forest (RF), Support Vector Machine (SVM), artificial neural networks (ANNs), and deep learning architectures is systematically compared with respect to input data characteristics, spatial and temporal scales, and model evaluation metrics. The reviewed evidence demonstrates that machine learning–based approaches, particularly those capable of capturing nonlinear relationships and integrating multi-source datasets, generally outperform conventional statistical and physics-based methods in predictive accuracy and modeling capability. Nevertheless, several important challenges remain, including data quality and uncertainty, the limited interpretability of complex models, and the need to better integrate data-driven techniques with the physical principles governing climate systems. Overall, this review provides a valuable reference for researchers and practitioners seeking to identify and implement appropriate machine learning approaches for climate hazard assessment, prediction, and risk management.