Development of Levenberg-Marquardt, Resilient Back-Propagation, and Conjugate Gradient Powell-Beale Artificial Neural Networks for Peak Urban Water Demand Forecasting in Nicosia, Cyprus

Cyprus is in the middle of an unprecedented water crisis that has lasted several years. Four ideas that have been considered to aid in resolving the problem include imposing effective water use restrictions, implementing water demand reduction programs, optimizing water supply systems, and developing alternative water source strategies. A critical component of each of these initiatives is the accurate forecasting of short- term peak water demands. This study compared multiple linear regression and three types of artificial neural networks (ANNs) as methods for peak weekly water demand forecast modeling. Analysis was performed on six 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. Twenty multiple linear regression models, twenty Levenberg-Marquardt ANN models, twenty Resilient Back- Propagation ANN models, and twenty Conjugate Gradient Powell-Beale ANN models were developed and their relative performance was compared. For both the Athalassa and Public Garden regions in Nicosia, the Levenberg-Marquardt ANN method was found to provide a more accurate forecast of peak weekly water demand than the other two types of ANNs and multiple linear regression. It was also found that the peak weekly water demand in Nicosia is better correlated with the rainfall occurrence rather than the amount of rainfall itself.