Natural Hazards

, Volume 77, Issue 2, pp 1081–1102 | Cite as

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

  • Zhiqiang Xie
  • Qingyun DuEmail author
  • Fu Ren
  • Xiaowei Zhang
  • Sam Jamiesone
Original Paper


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.


Artificial neural network Urban drainage system Urban waterlogging simulation Knowledge coupled MATLAB River stage forecast 



The authors are grateful to the 2011 Science and Technology Program of the Ministry of Housing and Urban–Rural Development of the People’s Republic of China, and to the 2010 Technology Project of the Kunming Science and Technology Bureau for its support. The study has also been supported by the National Natural Science Foundation of China (Project Nos. 41371427/D0108 and 41271455/D0108).


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Zhiqiang Xie
    • 1
    • 2
  • Qingyun Du
    • 1
    Email author
  • Fu Ren
    • 1
  • Xiaowei Zhang
    • 3
  • Sam Jamiesone
    • 4
  1. 1.School of Resource and Environmental ScienceWuhan UniversityWuhanChina
  2. 2.Kunming Underground Pipeline Detection and Management OfficeKunmingChina
  3. 3.Kunming University of Science and TechnologyKunmingChina
  4. 4.Heriot-Watt UniversityEdinburghUK

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