Abstract
Urban water demand is influenced by a variety of factors such as climate change, population growth, socio-economic conditions and policy issues. These variables are often correlated with each other, which may create a problem in building appropriate water demand forecasting model. Therefore, selection of the appropriate predictor variables is important for accurate prediction of future water demand. In this study, seven variable selection methods in the context of multiple linear regression analysis were examined in selecting the optimal predictor variable set for long-term residential water demand forecasting model development. These methods were (i) stepwise selection, (ii) backward elimination, (iii) forward selection, (iv) best model with residual mean square error criteria, (v) best model with the Akaike information criterion, (vi) best model with Mallow’s Cp criterion and (vii) principal component analysis (PCA). The results showed that different variable selection methods produced different multiple linear regression models with different sets of predictor variables. Moreover, the selection methods (i)–(vi) showed some irrational relationships between the water demand and the predictor variables due to the presence of a high degree of correlations among the predictor variables, whereas PCA showed promising results in avoiding these irrational behaviours and minimising multicollinearity problems.
Highlights
Water demand forecasting is a vital element in urban planning and sustainable development of a city
Seven variable selection methods in the context of linear regression (i.e., stepwise selection, forward selection, backward elimination, MSE criterion, Mallow’s Cp criterion, Akaike information criterion (AIC) criterion and principal component analysis (PCA)) were compared for long-term water demand forecasting for the Blue Mountains Water Supply System located in New South Wales, Australia
The results showed that different variable selection methods resulted in different sets of predictor variables
Summary
Water demand forecasting is a vital element in urban planning and sustainable development of a city. Many important decisions in regards to water demand management, environmental planning and optimum utilization of water resources depend on accurate water demand forecasting. Future water availability is expected to reduce in many urban cities [1] due to several factors such as population growth, changing climatic conditions, pollution of water, scarcity of untapped water sources and increased frequency of droughts [2,3]. It is important to have the accurate future water demand projections to ensure adequate water supply to the cities by adopting various strategies such as capacity expansion of existing water supply systems, building new infrastructure and implementation of water demand management policies. Urban water demand is influenced by a variety of factors such as demographic (e.g., number of population and number of dwellings), climatic (e.g., rainfall, temperature and evaporation), Water 2018, 10, 419; doi:10.3390/w10040419 www.mdpi.com/journal/water
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