Artificial intelligence for flood risk management: A comprehensive state-of-the-art review and future directions

Abstract

Climate hazards are escalating in frequency and severity, with flooding escalating as a major threat. The limitations of the existing analytical necessitate and computational tools for flood risk management necessitates a shift towards more data-driven flood risk management strategies informed by AI-driven tools and methods. This paper explores the forefront of flood risk management focusing on integrating artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL) technologies. By reviewing hundreds of relevant studies, we present a comprehensive analysis of AI applications in flood risk management by examining flood types, AI models, spatial scales, input data, and practical applications, to provide a holistic view of the current landscape and future potential of AI-enhanced flood risk management. We highlight the extent to which AI-driven solutions can complement the existing tools to enhance the reliability of flood predictions and inform mitigation and response strategies. The paper also address prevailing challenges, including data bias and the need for explainable AI models, and proposes pathways for future research to fully harness AI's potential in mitigating flood risks. The analysis underscores AI's promising potential in improving adaptive flood risk management, which is crucial for safeguarding communities and infrastructures against the escalating challenges posed by floods.