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Modelling urban floods is essential for disaster prevention, yet it faces limitations in accuracy due to technical, operational, and functional constraints. The study employs a primary market research analysis to explore the perspectives of both academic and non-academic experts in urban flood modelling (UFM). Identified issues include inadequate spatial and temporal model resolution, high data requirements, and non-intuitive user interfaces. Opportunities are recognized in integrating flood risks, social dynamics, future land use, climate data, and real-time information while reducing computational costs and improving usability. To address these aspects, a holistic framework has been proposed that includes features like hybrid-physics AI modelling, real-time data integration, compound flood simulation, transfer learning, sociohydrology tools, future scenario forecasting, cloud-based pipelines, interoperability, compatibility, and AI-enhanced parallel computing and user interface. Finally, we presented an ecosystem map illustrating stakeholder roles in UFM. The findings offer valuable insights into refining UFM for enhanced urban flood resilience.
Mahmood et al. (Mon,) studied this question.