Regional flood risk grading assessment considering indicator interactions among hazard, exposure, and vulnerability: A novel FlowSort with DBSCAN

Abstract

With escalating flood risks due to global warming and frequent extreme rainfall events, it is crucial to highlight the importance of flood risk assessment for devising prudent mitigation strategies and promoting sustainable development. Against this backdrop, this study proposes a novel regional flood risk grading assessment method, namely the Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-FlowSort method, aimed at comprehensively assessing flood risks in county-level regions of Anhui Province. The innovation of this method lies in its consideration of interactions among the hazard, exposure, and vulnerability subsystems, as well as the comprehensive determination of assessment indicator weights through the Probability Language Term Set-Decision Making Trial and Evaluation Laboratory (PLTS-DEMATEL) and Entropy Weight Method (EWM). Thus, the subjectivity and objectivity of the weights of the indicators are integratedly taken into account. Additionally, this study introduces DBSCAN to generate reference profiles, improving the reliance on expert input in the traditional FlowSort method and enhancing the automation and objectivity of the evaluation process. The results of the study demonstrate that the DBSCAN-FlowSort method exhibits superior classification performance in predicting flood hazards, particularly in accurately identifying and assessing high-risk areas when considering interactions among indicators in different subsystems. This method provides a new scientific tool for flood risk assessment and management, which is crucial for devising flood resource allocation and risk mitigation measures in both theory and practice.