The application of integrating comprehensive evaluation and clustering algorithms weighted by maximal information coefficient for urban flood susceptibility

• Published in: Journal of environmental management 2023-10, Vol.344, p.118846-118846, Article 118846
• Main Authors: Wang, Hongfa, Meng, Yu, Wang, Huiliang, Wu, Zening, Guan, Xinjian
• Format: Article
• Language: English
• Subjects: Analytic hierarchy process; Clustering algorithm; Comprehensive evaluation; Flood susceptibility; Maximal information coefficient; Weight assignment
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2023.118846

Different sub-regions of Zhengzhou city have various levels of sensitivity to flood due to the impact of urbanization. Thus, an accurate flood sensitivities assessment is a key tool for flood prevention and urban planning and development. To successfully link the urban flood sensitivity assessment with the real flood situation, a method combining clustering algorithm with comprehensive evaluation is presented. The proposed method is not affected by the classification standard of sensitivities levels and has a small and undemanding demand for flood data. First, Maximal Information Coefficient between conditional factors and flood is employed to determine the weight. Then, the different results are obtained by three clustering algorithms. Finally, a four-layer evaluation structure weighted by analytic hierarchy process is established to select the best flood susceptibility map. A case study in the Zhengzhou city, China shows that the positive scale amplification strategy is relatively best and the flood sensitivity of sub-regions in Zhengzhou city should be divided into four levels obtained by K-Means clustering. Hence, it supplies the valuable insights for the urban planning and flood mitigation. [Display omitted] •A method combining clustering algorithm with comprehensive evaluation to assess flood sensitivity was developed.•Maximal Information Coefficient was employed to measure the relationships of between geographical factors and flood.•The effect of various weighting strategies on clustering results was examined.•The matching degree between the clustering results and the actual flood distribution was evaluated comprehensively.•Four levels of flood sensitivities in study area were recommended.