A comparative assessment of variable selection methods in urban water demand forecasting

Haque, Md Mahmudul and Rahman, Ataur and Hagare, Dharma and Chowdhury, Rezaul Kabir (2018) A comparative assessment of variable selection methods in urban water demand forecasting. Water, 10 (4). pp. 1-15. ISSN 2073-4441

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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 Akaike information criteria, (vi) best model with Mallow’s Cp criteria 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) to (vi) showed some irrational relationships between the water demand and the predictor variables due to the presence of high degree of correlations among the predictor variables, whereas PCA showed promising results in avoiding these irrational behavior and minimising multicollinearity problems.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying
Date Deposited: 17 Apr 2018 01:11
Last Modified: 16 May 2018 00:03
Uncontrolled Keywords: variable selection; principal component analysis; multiple regression; multicollinearity; long term water demand forecasting; urban water
Fields of Research : 09 Engineering > 0905 Civil Engineering > 090509 Water Resources Engineering
Socio-Economic Objective: D Environment > 96 Environment > 9609 Land and Water Management > 960912 Urban and Industrial Water Management
Identification Number or DOI: 10.3390/w10040419
URI: http://eprints.usq.edu.au/id/eprint/33938

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