A comparison of linear and nonlinear regression modelling for forecasting long term urban water demand : a case study for Blue Mountains Water Supply System in Australia

Prediction of long term water demand is necessary to assess the future adequacy of water resources, to attain an efficient allocation of water supplies among competing water users and to ensure long-term water sustainability. In order to predict future water demand and assess the effects of future climate and other factors on water demand, suitable mathematical models are needed. The study compares a multiple linear and nonlinear regression model to forecast monthly water demand in the Blue Mountains Water Supply System, Australia. The performance of the developed models are assessed through the relative error (RE), the coefficient of determination (R2), the percent bias (PBIAS) and the accuracy factor (Af), computed from the observed and model predicted water demand values. The RE, R2, PBIAS, Af , values are found to be 0.46%, 0.88, 2.07% and 1.04, respectively for multiple linear regression model and 2.49%, 0.30, -20.79% and 1.21, respectively for multiple nonlinear regression model. The results of the study show that the developed multiple linear regression model is capable of predicting water demand more accurately than multiple nonlinear regression model.