Investigating the Factors Affecting the Accuracy of Water Meters and Providing Appropriate Approach for Replacing them Using Cluster Analysis and Artificial Neural Networks

Non-Revenue Water(NRW) is one of the most important problems that beneficiaries companies are trying to reduce. Most of researches have been done in this subject, focused on actual losses and leakage in water conveyance and distribution systems and less attention has been paid to the second component of NRW and Precision of Measurement Equipment (PME) yet. In this research, due to the lack of a clear scientific knowledge about the replacement of Water Meters(WM) in national water and wastewater engineering companies, field studies have been carried out on the PME and errors of subscribers' WM and the performance range of the WMs was compared using a new approach. At first, the identification of effective parameters on the accuracy of the WM was investigated and then by clustering method, the WM were compared and the effect of each parameter on the accuracy of the WM was determined. In order to predicting the performance and accuracy of the WM, Artificial Neural Network(ANN) algorithm was used and for optimizing the weights and biases matrix, Genetic Algorithm(GA) was used too