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

作　　者：LAI Wen-li WANG Hong-rui WANG Cheng ZHANG Jie ZHAO Yong 

机构地区：[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 Riuer Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

出　　处：《Journal of Mountain Science》2017年第5期898-905,共8页山地科学学报（英文）

基　　金：supported by the National Key R&D Program of China (GrantN o.2016YFC0401407);National Natural Science Foundation of China (Grant Nos. 51479003 and 51279006)

摘　　要：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 haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing 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-ANNis 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 Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.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 haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing 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-ANNis 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 Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.

关 键 词：Waterlogging risk assessment Self-organizing map(SOM) neural network Urban storm 

分 类 号：P426.616[天文地球—大气科学及气象学] TU992[建筑科学—市政工程]