Ai-driven approaches to flood risk management: overcoming data bias and enhancing decision-making

The growing number and severity of climate-related hazards have made flooding an important issue. It is crucial to use new, data-driven approaches to flood risk management (FRM) due to the constraints of traditional analytical and computational methodologies. The purpose of this study is to investigate the impact that artificial intelligence (AI), specifically machine learning and deep learning approaches, has on the enhancement of FRM. This assessment, which draws on over 300 papers on artificial intelligence in FRM, examines various types of floods, spatial scales, AI models, input data reports, and practical applications. One of the primary objectives is to develop AI-driven solutions that enhance flood estimations and early detection and response systems, working in conjunction with existing technology. This paper demonstrates how AI can help improve adaptive FRM and protect infrastructure and communities from the increasing risks of floods by addressing these challenges and exploring various possibilities for further research. According to the results, AI may significantly enhance flood risk predictions and provide more accurate real-time evaluations of floods. A variety of data types, including satellite images, hydrological data, and real-time weather reports, can be processed by machine learning and deep learning models. These models help to improve decision-making and emergency response by providing short-term flood estimates. Major obstacles to their broad use, however, include data bias, the difficulty of interpreting models, and the requirement for substantial computational resources. Moreover, this research suggests several policy changes. It is crucial to develop open-source AI systems that can be utilised in flood-prone areas with diverse socioeconomic contexts. Second, policymakers should prioritise expanding access to and inclusion of AI-driven solutions, particularly for marginalized and disadvantaged populations. Ultimately, it is crucial to make AI models more transparent and explainable to enhance trust among stakeholders and ensure that FRM decisions are well-informed..