A Mining Framework for Efficient Leakage Detection and Diagnosis in Water Supply System

Abstract

A smart city smart meter water grid have to be reliable and capable to safeguarding the 24 * 7 trustworthy water distribution network that guarantees less wastage by leakages in the pipeline. Distributors and Consumers are turning to the Internet of Things and deep learning to meet requirement. Continuously monitoring the system and taking requirements manually is tedious job. Smart nodes with hall sensors provide continuous measurements and warehoused in database captured from the smart city water distribution network using smart meters. This paper deals with detection of leakages using deep learning technique. In order to find out leakage estimation and exact leakage position in water distribution pipelines the proposed framework uses the pulse rate, flow rate and quantity as prime attributes. Experiments carried had exhibit the significance of deep learning in leakage detection.