Assessing subway station flood risks with a resilience Framework: Combining weighting methods and two-dimensional cloud model

Abstract

This study proposes a resilience-based flood risk assessment framework for subway stations. Grounded in resilience theory, the TOSE-4Rs framework systematically identifies risk factors across Technical, Organizational, Social, and Economic dimensions, establishing a comprehensive evaluation index system. A combined weighting approach integrating Lagrange optimization, the C-OWA operator, and entropy weighting mitigates subjective or objective bias inherent in single methods. Addressing the probability and loss of flood disasters concurrently in the subway station, a two-dimensional cloud model quantifies assessment uncertainty and fuzziness. Risk levels are determined by comparing evaluation cloud maps against standard evaluation cloud map, with closeness degree validating result accuracy. The new method is applied to the flood risk analysis of 8 subway stations in the Changsha Subway, and the calculation results show that: (i) The result was consistent with the actual situation. And compared with the FAHP, which validates the applicability and accuracy of the employed methodology. (ii) Based on assessment results, corresponding flood control measures are put forward for subway stations with higher risk assessment levels. Therefore, this new method has high theoretical value and practical significance and can provide a reference for the flood risk assessment of subway stations in other engineering fields.