Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms

Abstract

For the past several years, Cyprus has been facing an unprecedented water crisis. Four options that have been considered to help resolve the problem of drought in Cyprus include imposing effective water use restrictions, implementing water-demand reduction programs, optimizing water supply systems, and developing sustainable alternative water source strategies. An important aspect of these initiatives is the accurate forecasting of short-term water demands, and in particular, peak water demands. This study compared multiple linear regression and three types of multilayer perceptron artificial neural networks (each of which used a different type of learning algorithm) as methods for peak weekly water-demand forecast modeling. The analysis was performed on 6 years of peak weekly water-demand data and meteorological variables (maximum weekly temperature and total weekly rainfall) for two different regions (Athalassa and Public Garden) in the city of Nicosia, Cyprus. 20 multiple linear regression models, 20 L...