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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 Du
  • Fu Ren
  • Xiaowei Zhang
  • Sam Jamiesone
Original Paper

Abstract

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.

Keywords

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

Notes

Acknowledgments

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).

References

  1. Artigue G, Johannet A, Borrell V, Pistre S (2012) Flash flood forecasting in poorly gauged basins using neuralnetworks: case study of the Gardon de Mialet basin. Nat Hazards Earth Syst Sci 12:3307–3324CrossRefGoogle Scholar
  2. Caihong S, Zongxue X (2006) Application of artificial neural network model in flood forecast downstream the Wei River. J China Hydrol 26(2):38–42Google Scholar
  3. Castellet EBI, Gómez VM, Ripollés JD (2006) Decision support system for flood risk assessment and management. In: 7th International Conference on Hydroinformatics, Nice, FranceGoogle Scholar
  4. Chen M (2013) Introduction to MATLAB neural network principle and examples. Tsinghua University Press, BeijingGoogle Scholar
  5. Chen JC, Ning SK, Chen HW, Shu CS (2008) Flooding probability of urban area estimated by decision tree and artificial neural networks. J Hydroinformatics 10:57–67CrossRefGoogle Scholar
  6. Chiang YM, Chang LC, Tsai MJ, Wang YF, Chang FJ (2010) Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites. Hydrol Earth Syst Sci 14:1309–1319CrossRefGoogle Scholar
  7. Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25:80–108CrossRefGoogle Scholar
  8. Deng K, Liu L (2008) Analysis of influence of calculation parameters on the discharge of drain pipeline. Water Supply Drain 34:32–35 (in Chinese)Google Scholar
  9. Duncan A, Chen AS, Keedwell E, Djordjević S, Savić D (2012) Urban flood prediction in real-time from weather radar and rainfall data using artificial neural networks. IAHS Publ 568–573Google Scholar
  10. Fu Y, Zhang C (2013) Application of artificial neural network in urban waterlogging. Constr Des Pro 2:154–157Google Scholar
  11. Hung NQ, Babel MS, Weesakul S, Tripathi NK (2009) An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol Earth Syst Sci 13:1413–1425CrossRefGoogle Scholar
  12. Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2011) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67(1):251–264Google Scholar
  13. Lihua F (2000) Study of flood forecast based on neural network. J Nat Disasters 9(2):45–48Google Scholar
  14. Liu J, Zhang Z, Wu J (2005) Forecast model of urban flood situation based on neural network. Comput Eng Des 26(3):699–701Google Scholar
  15. Pradhan B (2009) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9:1–18Google Scholar
  16. Savic DA, Bicik J, Morley MS, Duncan A, Kapelan Z, Djordjevic S, Keedwell EC (2013) Intelligent urban water infrastructure management. J Indian Inst Sci 93:319–336Google Scholar
  17. See L, Corne S, Dougherty M, Openshaw S (1997) Some initial experiments with neural network models of flood forecasting on the River Ouse. GeoComputation ‘97 & SIRC’97, 1997, 15–22Google Scholar
  18. Varoonchotikul P (2003) Flood forecasting using artificial neural networks. The Chemical Rubber Company Press, Boca RatonGoogle Scholar
  19. Zhao W (2009) Intelligent evaluation model study of urban flood disaster system. Master’s Thesis, Hefei University of TechnologyGoogle Scholar
  20. Zhu X, Lu C, Wang R, Bai J (2005) Forecast model of flood level based on artificial neural network. J Hydraul Eng 36(7):1–8Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Zhiqiang Xie
    • 1
    • 2
  • Qingyun Du
    • 1
  • 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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