Urban Flood Resilience Evaluation Based on Heterogeneous Data and Group Decision-Making

• Published in: Entropy (Basel, Switzerland) Vol. 26; no. 9; p. 755
• Main Authors: He, Xiang, Hu, Yanzhu, Yang, Xiaojun, Wang, Song, Wang, Yingjian
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.09.2024; MDPI
• Subjects: Analysis; Analytic hierarchy process; China; Climate change; Decision making; Disasters; Ecosystems; Floods; Fuzzy sets; group decision-making; heterogeneous data; indicator system; Indicators; Methods; Multiple criterion; normal cloud model; Population density; Public transportation; Qualitative analysis; Resilience; Transportation services; urban flood resilience; Weighting methods; China; normal cloud model; indicator system; urban flood resilience; group decision-making; heterogeneous data
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e26090755

In recent years, urban floods have occurred frequently in China. Therefore, there is an urgent need to strengthen urban flood resilience. This paper proposed a hybrid multi-criteria group decision-making method to assess urban flood resilience based on heterogeneous data, group decision-making methodologies, the pressure-state-response model, and social-economic-natural complex ecosystem theory (PSR-SENCE model). A qualitative and quantitative indicator system is formulated using the PSR-SENCE model. Additionally, a new weighting method for indicators, called the synthesis weighting-group analytic hierarchy process (SW-GAHP), is proposed by considering both intrapersonal consistency and interpersonal consistency of decision-makers. Furthermore, an extensional group decision-making technology (EGDMT) based on heterogeneous data is proposed to evaluate qualitative indicators. The flexible parameterized mapping function (FPMF) is introduced for the evaluation of quantitative indicators. The normal cloud model is employed to handle various uncertainties associated with heterogeneous data. The evaluations for Beijing from 2017 to 2021 reveal a consistent annual improvement in urban flood resilience, with a 14.1% increase. Subsequently, optimization recommendations are presented not only for favorable indicators such as regional economic status, drainability, and public transportation service capacity but also for unfavorable indicators like flood risk and population density. This provides a theoretical foundation and a guide for making decisions about the improvement of urban flood resilience. Finally, our proposed method shows superiority and robustness through comparative and sensitivity analyses.