Using and Development of Regression Models for Predicting Pipes Failure Rate in Water Distribution Networks

Pipes failure events in the water distribution networks provide leakage of water. Failures cause the loss of significant fresh water and investments losses. The most important parameters are: material, age, length, diameter and hydraulic pressure. In this paper four statistical methods have used for analyzing pipe incidents, with the goal of estimation of failure probability in future, with finding the most influences parameters in the incidents. The statistical regression models using in this research are linear regression model, exponential regression model, Poisson regression model , and Logistic regression model . For evaluation of the models, the data of a pilot in the first district of the Tehran’s Water and Wastewater Company with more than 48500 consumers, total pipe length of 582702 meter, different materials and diameters were used. The results demonstrated that the logistic model has a better performance than others to predict the future events with a higher probability.