Prediction of water utility performance: The case of the water efficiency rate

Abstract

This paper deals with the development of a decision-aiding model for predicting, in an ex-ante way, the effects of a mix of actions on an asset and on its operation. The objective is then to define a compromised policy between costs and performance improvements. We investigate the use of multiple regression analysis (MRA) and an artificial neural network (ANN) to establish causal relationships between the network efficiency rate, and a set of explanatory variables on one hand, and potential water loss management actions such as leak detection, maintenance and asset renewal, on the other hand. The originality of our approach is in developing a two-step ex-ante model for predicting the efficiency rate involving low and high level explanatory variables in a context of unavailability of data at the scale of the water utility. The first step exploits a national French database «SISPEA» (Système d'Information d'information sur les Services Publics d'Eau et d'Assainissement) to calibrate a general prediction model that establishes a correlation between efficiency (output) and other performance indicators (inputs). The second step involves the utility manager to build a causal model between endogenous and exogenous variables of a specific water network (low level) and performance indicators considered as inputs for the previous step (high level). Uncertainty is taken into account by Monte Carlo simulations. An application of our decision model on a water utility in the southeast of France is provided as a case study.