Review article: Harnessing Machine Learning methods for climate multi-hazard and multi-risk assessment

Abstract. In recent years, interest in data-driven methods, such as machine learning and multivariate statistics for multi-hazard and multi-risk assessment has surged, due to their ability to integrate vast amounts of data in modelling complex non-linear relationships between hazard and risk factors. This review explores data-driven methods in climate multi-hazard and risk analysis, focusing on four themes: (i) data processing and collection; (ii) hazard identification, prediction and analysis; (iii) risk analysis; and (iv) future risk scenarios under climate change. Key findings highlight the extensive use of machine learning to combine Earth observations and climate data for downscaling and land use and land cover characterisation; the application of deep learning for hazard prediction; the use of ensemble methods for risk analysis; and the growing emphasis on explainable AI frameworks. Training of supervised machine learning approaches on past impacts to model future risk through climate projections also emerged as a significant area. Future research should prioritize multi-hazard interactions, particularly triggering and cascading effects, integrate dynamic vulnerability and exposure factors, and address uncertainties associated with using machine learning for extrapolation. Advancements in Earth observations and textual data integration, alongside the development of open-access disaster catalogues, will be crucial for improving multi-risk analyses and supporting AI-driven early warning systems tailored to regional needs.