Water Demand Modelling Using Independent Component Regression Technique

Water demand modelling is an active field of research. The modelling and forecasting tools are useful to get the estimation of forecasted water demand for different forecast horizons (e.g. 1 h to 10 years) in order to achieve more efficient and sustainable water resources management systems. However, modelling and forecasting of accurate water demand are challenging and difficult tasks. Several issues make the demand forecasting challenging such as the nature and quality of available data, numerous water demand variables, diversity in forecast horizons and geographical differences in modelling catchments. These issues have motivated a number of studies to be conducted to produce better water demand modelling and forecasting tools in order to improve forecast reliability. A variety of techniques have been adopted in water demand forecasting, however, application of independent component regression (ICR) technique has not been investigated yet. Hence, this study explores, for the first time, the use of the ICR technique in medium term urban water demand forecasting. This uses data from the city of Aquidauana, Brazil. It also compares the performance of the developed ICR model with two other commonly modeling methods, principal component regression and multiple linear regression models. It has been found that ICR model perform better than the other two models in modelling water demand with a higher performance indices (i.e. R2, RMSE, NSE and MARE) for the independent validation period. The results indicate that the ICR technique has the potential to develop water demand models successfully. The methodology adopted in this paper can be applied to other countries to develop water demand forecasting model.

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