Using Artificial Neural Networks to Predict Leakage Location in Water Distribution Networks

Abstract

One of the major types of water wastage in a distribution network is the leakage that occurs in pipes or other network components such as connections. Leaks in urban water supply networks impose many costs and workforce on governments and related organizations annually. If the cause of leakage is traced to the cracks and slits on various parts of a network, the amount of leakage from the location of the crack is said to be directly associated with the amount of pressure at that point. To trace leakage in water distribution networks, this study introduced a method based on hydraulics modeling and the inverse solution of flow equations to predict the location and amount of leakage in water distribution networks, by considering the values of the measured pressure in some network nodes. For this, a hydraulic model of the network under study was provided in the EPANET Hydraulic Analysis software, and the network analysis of various values and states of hypothetical leaks led to obtaining pressure values in the different nodes of the network via modeling water modeling under a steady state. Then, the artificial neural network, having been trained, was used to provide measured pressures in some network nodes at the testing hour as input data to the neural network to locate possible leaks in the water distribution network and to predict their approximate values. The findings were found to enjoy desirable accuracy. DOI: https://doi.org/10.52783/pst.835