Intelligent Soft Computing Models in Water Demand Forecasting
Water Stress in Plants
Given the increasing trend in water scarcity, which threatens a number of regions worldwide, governments and water distribution system WDS operators have sought accurate methods of estimating water demands. While investigators have proposed stochastic and deterministic techniques to model water demands in urban WDS, the performance of soft computing techniques [e.g., Genetic Expression Programming GEP ] and machine learning methods [e.g., Support Vector Machines SVM ] in this endeavour remains
... endeavour remains to be evaluated. The present study proposed a new rationale and a novel technique in forecasting water demand. Phase space reconstruction was used to feed the determinants of water demand with proper lag times, followed by development of GEP and SVM models. The relative accuracy of the three best models was evaluated on the basis of performance indices coefficient of determination R , mean absolute error MAE , root mean square of error RMSE , and Nash-Sutcliff coefficient E . Results showed GEP models were highly sensitive to data classification, genetic operators, and optimum lag time. The SVM model that implemented a Polynomial kernel function slightly outperformed the GEP models. This study showed how phase space reconstruction could potentially improve water demand forecasts using soft computing techniques.