In this paper, the focus is on the transformative role of AI-driven decision support systems in flood prediction and management. The systems leverage artificial intelligence algorithms to analyze various data sources, addressing challenges such as data scarcity and uncertainty. Continuous research and innovation are highlighted as crucial elements in overcoming existing limitations and enhancing the effectiveness of these systems. The paper also touches upon the importance of integrating emerging technologies like block chain and edge computing to improve the efficiency and reliability of flood management systems. By doing so, stakeholders can leverage secure and decentralized data storage, as well as real-time data processing at the network edge, contributing to more effective decision-making and response strategies. Furthermore, the abstract emphasizes the potential for AI to revolutionize environmental engineering practices, particularly in the context of flood management. The interdisciplinary collaboration and stakeholder engagement are highlighted as essential components for developing holistic and adaptive strategies. By bringing together experts from various fields, these strategies aim to build resilient communities capable of withstanding the increasing challenges posed by climate change and natural disasters. In summary, the abstract provides a concise overview of the key themes, including the role of AI-driven decision support systems, continuous research and innovation, integration of emerging technologies, and the potential for AI to revolutionize environmental engineering practices for more effective flood prediction and management.
Ihwughwavwe et al. (Mon,) studied this question.