Support Vector Machines in Urban Water Demand Forecasting Using Phase Space Reconstruction

High complexity of water distribution systems' (WDS) dynamics has convinced researchers to look for more sophisticated statistical approaches in urban water demand forecasting. Given the huge threat to major water reserves and ongoing draughts, water authorities are concerned with long term analysis of water demand to deal with uncertain future of this dynamic infrastructure. Researchers have tried a wide range of modelling techniques to propose an accurate model. However, applications of machine learning techniques are yet to be explored in detail. This research proposes a support vector machine (SVM) model, using polynomial kernel function to forecast monthly water demand of City of Kelowna (CKD), Canada. The prime objective of this research is to assess the use of phase space reconstruction prior to design of models' input variables combinations. Results of this study proved optimum lag time of the input variables can significantly improve the performance of SVM models.