Abstract
Accurate forecast of water demand is very crucial in developing a water resource management strategy to check the balance of future water supply and demand to ensure proper water supplies to the people. In order to forecast water demand, different models have been adopted in the literature. Among these the multiple regression analysis is quite popular for long term water demand forecasting. In spite of their evident success in modelling water demands, it can face difficulties in the case of multicollinearity, which implies highly correlated variables. Since water demand depends on many factors such as population, household size, rainfall, temperature, age of population, education, water price and policy, a multicollinearity problem may arise during the development of a multiple regression model which may lead to the incorrect estimation of future water demand. To avoid multicollinearity problem, principal component regression analysis has been used in several environmental studies which demonstrated its ability to eliminate the multicollinearity problem and to produce better model results. However, application of principal component regression in water demand forecasting is limited. In this study, principal component regression model was developed by combining multiple linear regression and principal component analysis to forecast future water demand in the Blue Mountains Water Supply systems in New South Wales, Australia. In addition, performances of the developed principal component regression model were compared with multiple linear regression model by adopting several model evaluation statistics such as relative error, bias, Nash-Sutcliffe efficiency and accuracy factor. It was found that the developed principal component regression model was able to predict future water demand with a higher degree of accuracy with an average relative error, bias, Nash-Sutcliffe efficiency and accuracy factor values of 3.47%, 2.92%, 0.44 and 1.04, respectively. Moreover, it was found that the principal component regression model performed better than the multiple linear regression model and could be used to eliminate the multicollinearity problem. The method presented in this paper can be adapted to other cities in Australia and the world.
Original language | English |
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Pages (from-to) | 49-59 |
Number of pages | 11 |
Journal | Journal of Hydrology and Environmental Research |
Volume | 1 |
Issue number | 1 |
Publication status | Published - 2013 |
Keywords
- water demand management
- water-supply
- Blue Mountains (N.S.W.)
- multicollinearity
- infrastructure (economics)
- environment and sustainability
- environmental sciences
- Centre for Western Sydney
- New South Wales
- Australia