Abstract
Balancing the supply and demand of urban flood regulation services is crucial for refining flood management policies. Previous studies often employed coarse evaluation units and data limitations may constrained assessments of flood regulation service demand, while this study employs finer evaluation units and an integrated methodology using open data. Taking Xiamen as a case study, machine learning and a comprehensive multi-criteria evaluation model were used to assess flood regulation service demand, hydrological modeling was conducted to evaluate the service supply. Spatial supply-demand disparities were analyzed by the supply-demand ratio. Results reveal pronounced spatial mismatches between flood regulation service supply and demand in Xiamen, with supply deficient units primarily concentrated in intense constructed areas, specifically Jimei, Huli, and Siming districts. The transferability of the method is examined, the correlation between supply-demand relationships and land use is analyzed, demonstrating the applicability of the methods in supporting similar studies in poor-data areas. The methodology could thereby facilitate spatial planning optimization and policy adjustments.
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