| Back-Propagation Artificial Neural Networks for Water Supply Pipeline Model |
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| 作者姓名： | 朱东海 张土乔 毛根海 |
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| 作者单位： | ZHU Donghai，ZHANG Tuqiao，MAO Genhai Department of Computer Science and Technology，Tsinghua University，Beijing 100084，China; Department of Civil Engineering，Zhejiang University，Hangzhou 310027，China |
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| 摘 要： | IntroductionWater supply pipelines are the lifelines of a city.Their operation status will greatly influence socialdevelopment.Burst points in pipeline networksshould be found as soon as possible. Due to their randomness,time variation,andperiodicity of urban water supply pipelines,it isvery difficult to locate burst points usingtraditional methods such as direct observing,sonardetecting,etc. The traditional methods cannotfind the burst point without frequent routinechecks.Sometimes,it is im…
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Back-Propagation Artificial Neural Networks for Water Supply Pipeline Model |
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| Abstract: | Water supply pipelines are the lifelines of a city. When pipelines burst, the burst site is difficult to locate by traditional methods such as manual tools or only by watching. In this paper, the burst site was identified using back-propagation (BP) artificial neural networks (ANN). The study is based on an indoor urban water supply model experiment. The key to appling BP ANN is to optimize the ANN's topological structure and learning parameters. This paper presents the optimizing method for a 3-layer BP neural network's topological structure and its learning parameters-learning ratio and the momentum factor. The indoor water supply pipeline model experimental results show that BP ANNs can be used to locate the burst point in urban water supply systems. The topological structure and learning parameters were optimized using the experimental results. |
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| Keywords: | back-propagation artificial neural network (BP ANN) learning ratio momentum factor water supply pipelines model experiment |
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