NDLI: 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

Content Provider	Springer Nature Link
Author	Xie, Zhiqiang Du, Qingyun Ren, Fu Zhang, Xiaowei Jamiesone, Sam
Copyright Year	2015
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.
Starting Page	1081
Ending Page	1102
Page Count	22
File Format	PDF
ISSN	0921030X
Journal	Natural Hazards
Volume Number	77
Issue Number	2
e-ISSN	15730840
Language	English
Publisher	Springer Netherlands
Publisher Date	2015-02-28
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural network Urban drainage system Urban waterlogging simulation Knowledge coupled MATLAB River stage forecast Natural Hazards Hydrogeology Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Civil Engineering Environmental Management
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Water Science and Technology

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