IMHE OpenIR  > Journal of Mountain Science  > Journal of Mountain Science-2017  > Vol14 No.5
Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: a case study of an urban stormin Beijing
LAI Wen-li; WANG Hong-rui; WANG Cheng; ZHANG Jie; ZHAO Yong
Corresponding AuthorWANG Hong-rui
2017-05
Source PublicationJournal of Mountain Science
ISSN1672-6316
Volume14Issue:5Pages:898-905
Subtype期刊论文
AbstractDue 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.
KeywordWaterlogging Risk Assessment Self-organizing Map (Som) Neural Network Urban Storm
DOI10.1007/s11629-016-4035-y
Indexed BySCI
Language英语
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/18715
CollectionJournal of Mountain Science_Journal of Mountain Science-2017_Vol14 No.5
Recommended Citation
GB/T 7714
LAI Wen-li,WANG Hong-rui,WANG Cheng,et al. Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: a case study of an urban stormin Beijing[J]. Journal of Mountain Science,2017,14(5):898-905.
APA LAI Wen-li,WANG Hong-rui,WANG Cheng,ZHANG Jie,&ZHAO Yong.(2017).Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: a case study of an urban stormin Beijing.Journal of Mountain Science,14(5),898-905.
MLA LAI Wen-li,et al."Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: a case study of an urban stormin Beijing".Journal of Mountain Science 14.5(2017):898-905.
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