A probabilistic GIS-based framework for urban flood risk assessment in Chengdu metro network
Qiuping Li, Ting Ni, Lei Li, Hao Wen, Lei Xu
Abstract
Urban metro network is highly vulnerable to flooding, yet most existing risk assessments rely on deterministic weighting methods that inadequately address uncertainty. To address this limitation, this study develops a probabilistic GIS-based flood risk assessment framework that integrates the Analytic Hierarchy Process (AHP) with its Monte Carlo extension (MC-AHP). Unlike conventional studies, the framework (i) explicitly quantifies uncertainty in expert judgments through β-PERT simulations, (ii) incorporates a comprehensive indicator system covering hazard, exposure, and vulnerability-including passenger flow and under-construction lines often overlooked in prior work, and (iii) provides spatially explicit risk mapping for both operational and planned infrastructure. Application to the Chengdu metro network reveals a concentric flood risk distribution, with the central urban core exhibiting the highest vulnerability despite moderate hazard levels. Compared to AHP, MC-AHP identifies a greater proportion of high-risk areas and provides probability distributions of indicator weights, thereby reducing the influence of subjective bias. Validation against historical flood events confirms the framework's predictive reliability. By explicitly incorporating uncertainty and extending analysis to planned infrastructure, this study advances the methodological rigor of urban flood risk assessment and provides generalizable insights for strengthening metro network resilience worldwide in the face of accelerating climate change and rapid urbanization.