Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China

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• 作者：Zhiqiang Xie ; Qingyun Du ; Fu Ren ; Xiaowei Zhang ; Sam Jamiesone
• 关键词：Artificial neural network ; Urban drainage system ; Urban waterlogging simulation ; Knowledge coupled ; MATLAB ; River stage forecast
• 刊名：Natural Hazards
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：77
• 期：2
• 页码：1081-1102
• 全文大小：2,096 KB
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• 作者单位：Zhiqiang Xie (1) (2)
  Qingyun Du (1)
  Fu Ren (1)
  Xiaowei Zhang (3)
  Sam Jamiesone (4)
  
  1. School of Resource and Environmental Science, Wuhan University, No. 129 Luoyu Road, Wuhan, China
  2. Kunming Underground Pipeline Detection and Management Office, Kunming, China
  3. Kunming?University?of?Science?and?Technology, Kunming, China
  4. Heriot-Watt University, Edinburgh, UK
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geophysics and Geodesy
  Geotechnical Engineering
  Civil Engineering
  Environmental Management
  
• 出版者：Springer Netherlands
• ISSN：1573-0840

文摘

Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7?% of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations.