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http://dx.doi.org/10.3741/JKWRA.2021.54.8.599

Development of leakage detection model in water distribution networks applying LSTM-based deep learning algorithm  

Lee, Chan Wook (Department of Civil Engineering, The University of Suwon)
Yoo, Do Guen (Institute of River Environmental Technology, Department of Civil Engineering, The University of Suwon)
Publication Information
Journal of Korea Water Resources Association / v.54, no.8, 2021 , pp. 599-606 More about this Journal
Abstract
Water Distribution Networks, one of the social infrastructures buried underground, has the function of transporting and supplying purified water to customers. In recent years, as measurement capability is improved, a number of studies related to leak recognition and detection by applying a deep learning technique based on flow rate data have been conducted. In this study, a cognitive model for leak occurrence was developed using an LSTM-based deep learning algorithm that has not been applied to the waterworks field until now. The model was verified based on the assumed data, and it was found that all cases of leaks of 2% or more can be recognized. In the future, based on the proposed model, it is believed that more precise results can be derived in the prediction of flow data.
Keywords
Water distribution networks; Leakage detection model; Deep learning;
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