标題：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 PanlongRiver,Kunming,China作者：Zhiqiang Xie, Qingyun Du, Fu Ren, Xiaowei Zhang, Sam Jamiesone

來源出版物：Natural Hazards DOI：10.1007/s11069-015-1648-3 出版年：Feb. 2014

摘要：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 ofPanlongRiveron 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.

文獻類型：Article

語種：English

作者關鍵詞：Artificial neural network;Urban drainage system;Urban waterlogging simulation;Knowledge coupled;MATLAB;River stage forecast

電子郵件地址：xzq_2010@126.com;qingyundu@gmail.com

地址：

[Zhiqiang Xie, Qingyun Du, Fu Ren]SchoolofResourceand Environmental Science,WuhanUniversity,No. 129 Luoyu Road,Wuhan,China

[Zhiqiang Xie]KunmingUnderground Pipeline Detection and Management Office,Kunming,China

[Xiaowei Zhang]KunmingUniversityof Science and Technology,Kunming,China

[Sam Jamiesone]Heriot-WattUniversity,Edinburgh,UK

ISSN：0921-030X

e-ISSN：1573-0840