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

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(SO...

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Bibliographic Details
Published in:Journal of mountain science 2017-05, Vol.14 (5), p.898-905
Main Authors: Lai, Wen-li, Wang, Hong-rui, Wang, Cheng, Zhang, Jie, Zhao, Yong
Format: Article
Language:English
Subjects:
Artificial neural networks
Case studies
Classification
Disasters
Earth and Environmental Science
Earth Sciences
Ecology
Environment
ENVIRONMENTAL SCIENCES
Geography
Neural networks
Population density
Rain
Rainfall
Residential density
Risk analysis
Risk assessment
Risk factors
Self organizing maps
self-organizing map (SOM) neural network
Storms
Traffic congestion
Urban areas
urban storm
Urbanization
Waterlogging
waterlogging risk assessment
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Summary: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.
ISSN:1672-6316
1993-0321
1008-2786
DOI:10.1007/s11629-016-4035-y