A Spatial Improved-kNN-Based Flood Inundation Risk Framework for Urban Tourism under Two Rainfall Scenarios

Urban tourism has been suffering socio-economic challenges from flood inundation risk (FIR) triggered by extraordinary rainfall under climate extremes. The evaluation of FIR is essential for mitigating economic losses, and even casualties. This study proposes an innovative spatial framework integrating improved k-nearest neighbor (kNN), remote sensing (RS), geographic information system (GIS) to analyze sites. Shanghai, China, was selected as a case study. Tempo-spatial factors, including climate, topography, drainage, vegetation, soil, were generate several flood-related gridded indicators inputs into the framework. A likelihood mapped represent possible tourist sites moderate-heavy scenario extreme scenario. resultant map verified maximum extent merged RS images water bodies. outcomes deliver baseline scientific urban planners policymakers take cost-effective measures decreasing evading pressure on sustainable development tourism. improved-kNN-based provides innovative, effective, easy-to-use approach evaluate industry change.