1-D convolution neural network based leak detection, location and size estimation in smart water grid

• Published in: Urban water journal Vol. 20; no. 3; pp. 341 - 351
• Main Authors: Choudhary, Pooja, Botre, B. A., Akbar, S. A.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 16-03-2023; Taylor & Francis Ltd
• Subjects: Artificial neural networks; Convolution; Convolution Neural Network (1-D CNN); Data points; Detection; Internet of Things (IoT); Leak detection; leak location; leak size; Machine learning; Natural resources; Neural networks; programmable logic controller (PLC); Prototypes; Remote monitoring; Remote sensors; smart water grid (SWG); Supervisory Control and Data Acquisition (SCADA); Survival; Transportation networks; Transportation systems; Turbidity; Water; Water loss; Water transportation
• ISSN: 1573-062X; 1744-9006
• DOI: 10.1080/1573062X.2022.2164732

Water is one of the essential natural resources for survival, but the water transportation system faces significant challenges because of huge water loss due to leaky pipeline systems. An IoT based novel SWG prototype has been developed and reported in this work. The SWG comprises sensors and devices that can continuously and remotely monitor the pressure, temperature, flow, pH, turbidity, etc., of the water being transported. Moreover, a novel 1-D CNN model has been developed by creating an artificial leak on the pipeline that takes input data points as a chunk of 5-minute time series to the network and gives output in leak detection, location and size estimation simultaneously. Further, the developed model is compared with other state of the art machine learning techniques and the proposed model is found better in terms of accuracy which is 94.32%, 91.91% and 89.85% for leak detection, size estimation and location respectively.