Leak localization in water distribution networks using deep learning
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
This paper explores the use of deep learning for leak localization in Water Distribution Networks (WDNs) using pressure measurements. By using a training data set including enough samples of all possible leak localizations, a Convolutional Neural Network(CNN) can be used to learn the different pressure maps that characterized each leak localization. The generalization accuracy has validated and evaluated by means of a testing data set. All of considered training, validation,and also testing data include leak size uncertainty, nodal water demand uncertainty and sensor noise. An innovative approach is proposed to convert every pressure residuals map to an image in order to apply a CNN. In addition with the purpose of filtering the effects of uncertainty and noise a time horizon Bayesian reasoning approach is used over each time instant classification output by the CNN. The Hanoi District Metered Area (DMA) is considered as a case study to illustrate the performance of the proposed leak localization method.
CitationJavadiha, M. [et al.]. Leak localization in water distribution networks using deep learning. A: International Conference on Control, Decision and Information Technologies. "2019 6th International Conferenceon Control, Decision and Information Technologies (CoDIT’19): April 23-26, 2019, Le Cnam, Paris, France". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 1426-1431.