Data-driven urban waterlogging risk management approach considering efficiency-equity trade-offs and risk mitigation capability evaluation

Abstract

Because disaster reduction resources are always constrained, risk mitigation measures and policies need to be weighted toward efficiency or equity. Greater efficiency focuses on high-risk areas, and greater equity focuses on identifying additional potential risk areas. To address this efficiency-equity tradeoff decision, a data-driven urban waterlogging risk mitigation capability evaluation and improving framework was proposed for nonprofit operations and government bailouts. First, a novel evaluation index was defined, the F(α,β) score, to consider both precision and recall in the binary classification tasks and to seek an effective efficiency-equity trade-off, after which a two-person, zero-sum matrix game model was developed to calculate the α and β parameters and select the highest valued optimal model. Then, using this model, a risk probability map was generated for Wuhan, China, and data envelopment analysis was used to conduct a risk mitigation capability evaluation and identify the needed policy improvements. It was found that: (1) vulnerability was the primary risk-related factor, followed by adaptability and restorability; (2) more importance should be attached to equity than efficiency when implementing risk reduction capability evaluations, that is, authorities should reduce resource investments in extremely high-risk DEA-inefficient areas and identify a greater number of potential risk areas; (3) there were around 1.55% of extremely high-risk areas, which were mainly concentrated in the Jianghan and Wuchang districts and around 9% of high-risk areas, mainly located in the Hongshan district; and (4) northeastern and southwestern Jianghan district could be a valuable reference for urban waterlogging management and disaster reduction. However, excessive investment in transportation infrastructure and construction in DEA inefficient areas should be appropriately reduced.