Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: a case study of an urban stormin Beijing Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: a case study of an urban stormin Beijing

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Vol14 No.5: 898-905

Title】Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: a case study of an urban stormin Beijing

Author】LAI Wen-li 1,2 ; WANG Hong-rui 1,2*; WANG Cheng 3; ZHANG Jie1,2; ZHAO Yong4

Addresses】1 College of Water Sciences, Beijing Normal University, Beijing 100875, China; 2 Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China; 3 Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA; 4 State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

Corresponding author】henrywang@bnu.edu.cn

Citation】Lai WL, Wang HR, Wang C, et al. (2017) Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: a case study of an urban storm in Beijing. Journal of Mountain Science 14(5). DOI: 10.1007/s11629-016-4035-y

DOI】10.1007/s11629-016-4035-y

Abstract】Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters have occurred almost annually in the urban area of Beijing, the capital of China. Based on a self-organizing map (SOM) artificial neural network (ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product (GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANN is suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors, producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or V were located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.

Keywords】Waterlogging risk assessment; Self-organizing map (SOM) neural network; Urban storm