Unravelling artificial intelligence in resources planning and flood disaster mitigation

Flood disaster has become an increasingly complex global challenge as it poses a big threat to people’s life, infrastructure, economic development and several industrial activities. It necessitates the development of innovative solutions for the improved understanding of flood events as it adversely impacts human and their livelihood, infrastructure and business economies in the flood prone areas. AI and machine learning techniques have huge potential which can be harnessed to improve the understanding of growing frequency, extent, severity, and complexity of flood events in different regions. The present study delves into the burgeoning domain of AI techniques such as Generative AI, Explainable AI, and machine learning algorithms for their use through cloud computing in providing greater insights into the voluminous flood related meta data streaming from diverse multiple sources for developing decision-making tools for flood warning, flood preparedness, and flood resilience infrastructure information systems. The study shows that there is a significant increase in the use of these techniques in addressing a wide range of problems that concern the public at large, such as flood, health, real state, livelihood, etc. Based on the findings of rigorous literature review and case studies, the present study also identifies future key research directions that can serve as a guideline for unravelling the power of AI and machine learning algorithms in prediction, interpretation, and deciphering intricate relationships among variables, determinants and consequences associated with flood disaster and resources planning and management for mitigating the adverse consequences of the flood. The study would be useful to various stakeholders in making informed decisions through AI powered algorithms and tools for evolving effective, systematic and trustworthy management strategies for resources planning and mitigating flood disaster.Keywords: Artificial intelligence, Machine learning, Cloud computing, Flood disaster, Resources planning