Enhancing Urban Flood Vulnerability Mapping with Multi-Criteria Decision Analysis

Abstract

Urbanization, climate change, and hydrological variability have significantly heightened flood risks in metropolitan areas, particularly in low-lying and highly developed regions. The increasing frequency and severity of urban floods necessitate advanced spatial decision-making techniques for effective flood vulnerability mapping. This study applies Multi-Criteria Decision Analysis (MCDA) integrated with Geographic Information Systems (GIS) to assess urban flood susceptibility in Taman Sri Muda, Shah Alam, Malaysia, an area frequently affected by flash floods. The study aims to identify key factors contributing to urban flooding, develop a spatial flood hazard model, and propose mitigation strategies. Six critical parameters including land use and land cover, distance from the main channel, drainage density, rainfall distribution, elevation and slope were reclassified and assigned weights. The weighted overlay technique was employed to generate the flood vulnerability map, which was validated using historical flood records. The results indicate that 37.8% of the study area is classified as high flood risk, while 24.8% falls under moderate risk, 27.6% under low risk, and 9.8% under very low risk. The findings provide valuable insights for urban planners, emergency response teams, and policymakers in developing flood mitigation strategies, improving drainage infrastructure, and enhancing flood resilience. This study underscores the necessity for integrating real-time hydrological data and machine learning models to further improve flood prediction accuracy in future research.