Assessment of flash flood risk based on improved analytic hierarchy process method and integrated maximum likelihood clustering algorithm

• Published in: Journal of hydrology (Amsterdam) Vol. 584; p. 124696
• Main Authors: Lin, Kairong, Chen, Haiyan, Xu, Chong-Yu, Yan, Ping, Lan, Tian, Liu, Zhiyong, Dong, Chunyu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2020
• Subjects: algorithms; China; Comprehensive risk assessment; death; decision making; disasters; Flash flood; flood control; floods; geographic information systems; Guangdong Province; Improved AHP method; ISO-Maximum clustering algorithm; risk; risk assessment; rural areas; statistical analysis; urban areas; China; ISO-Maximum clustering algorithm; Flash flood; Comprehensive risk assessment; Guangdong Province; Improved AHP method
• ISSN: 0022-1694; 1879-2707; 1879-2707
• DOI: 10.1016/j.jhydrol.2020.124696

•A new framework to cope with data scarcity problem is developed for lager scale flash flood risk mapping.•Analytic hierarchy process is improved considering the objective variability of data.•Data mining technique overcomes the difficulty in defining risk ranks’ threshold. Flash floods are one of the most severe natural disasters throughout the world, and are responsible for sizeable social and economic losses, as well as countless injuries and death. Risk assessment, which identifies areas susceptible to flooding, has been shown to be an effective tool for managing and mitigating flash floods. The study aims to introduce the methods to determine the weights of the risk indices, and identify the different risk clusters. In this regard, we proposed a methodology for comprehensively assessing flash flood risk in a GIS environment, by the improved analytic hierarchy process (IAHP) method, and an integration of iterative self-organizing data (ISODATA) analysis and maximum likelihood (ISO-Maximum) clustering algorithm. The weight for each risk index is determined by the IAHP, which integrates the subjective characteristics with objective attributes of the assessment data. Based on the data mining technology, the integration of ISO-Maximum clustering algorithm derives a more reasonable classification. The Guangdong Province of China was selected for testing the proposed method’s applicability, and we used a receiver operating characteristics (ROC) curve approach to validate the modeling of the flash-flood risk distribution. The validation against the historical flash flood data indicates a high reliability of this method for comprehensive flash flood risk assessment. In order to verify the proposed method’s superiority, in addition, the technique for order performance by similarity to ideal solution (TOPSIS) and the weights-of-evidence (WE) methods are used for comparison with the IAHP and ISO-Maximum clustering algorithm method. Moreover, we analyzed and compared the regularity of flash floods in the rural and urban areas. This study not only provides a new approach for large-scale flash flood comprehensive risk assessment, but also assists researchers and local decision-makers in designing flash flood mitigation strategies.