Burst Area Identification of Water Supply Network by Improved DenseNet Algorithm with Attention Mechanism

Jing Cheng, Sen Peng (), Rui Cheng, Xingqi Wu and Xu Fang
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Jing Cheng: Tianjin University
Sen Peng: Tianjin University
Rui Cheng: Tianjin University
Xingqi Wu: Tianjin University
Xu Fang: Tianjin University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 14, No 5, 5425-5442

Abstract: Abstract Pipe burst events pose threats to the safety and stability of water supply networks. Deep learning models have been studied for burst location analysis in recent years, but the applicability of the methods needs further research to cope with different size and hydraulic status of pipe networks. A novel framework of burst area identification based on an improved DenseNet model is proposed in this paper. First, according to regional characteristics of hydraulic state caused by pipe burst events, pipe network is divided into monitoring areas. Then, a new model by fully linear DenseNet with attention mechanism (FA-DenseNet) is developed for effective feature extraction and burst area identification. Thirdly, Bayesian optimization algorithm is introduced to automatically optimize the hyperparameters of the deep learning model for better accuracy. The proposed framework was tested in a real-life example network with synthetic burst monitoring data, and the accuracy of burst area identification reaches 0.918. The model is also compared with a traditional BP neural network and a DenseNet model without attention mechanism, and the results show that FA-DenseNet is more robust with the respects of different pipe burst conditions, monitoring noise and the number of pressure monitoring points. The proposed framework provides water companies an applicable and effective method of pipe burst identification and inspection.

Keywords: Pipe burst area identification; Deep learning; Attention mechanism; Water supply network; FA-DenseNet (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03316-9

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