Data-Driven Leak Detection and Identification in Water Distribution Networks using Transductive Long Short-Term Memory

The Water distribution networks (WDN) experience substantial water loss due to leakage in pipelines caused by pipe aging or pipe bursts. The Machine Learning (ML) and Deep Learning (DL) based acoustic method which depends on signal information and limited diversity of available data. However, the existing methods fail to detect leak points when sudden change in the input data. To overcome this limitation, a Transductive- Long Short-Term Memory (LSTM) is proposed for leakage detection and identification in a water supply network. The dataset utilized for detection was collected from the residential area are fed as input to the proposed model. After that, the proposed model considers neighbor data points and a new sequence of data was formed for identifying the unknown leakage points in the pipeline. For tuning the parameters Adam optimizer is utilized. The experimental results of the proposed T-LSTM are evaluated by the performance metrics that attained $\mathbf{9 8. 3 \%}$ accuracy, $98.2 \%$ precision, $98.2 \%$ recall, and $\mathbf{9 8. 1 \%}$ F1-score which is greater than the existing methods like Resnet, Change Detection Test (CDT), Artificial Intelligence-Deep Neural Network (AI-DNN), Ensemble ML models, Convolutional Neural Network (CNN)SqueezeNet.