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Open AccessArticle

Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques

1
Centre for Research in Environmental, Coastal and Hydrological Engineering (CRECHE), Department of Civil Engineering, University of KwaZulu-Natal, Durban 4041, South Africa
2
Department of Mechanical and Biomedical Engineering, Bells University of Technology, Ota 112233, Nigeria
*
Author to whom correspondence should be addressed.
Resources 2019, 8(3), 156; https://doi.org/10.3390/resources8030156
Received: 4 March 2019 / Revised: 21 August 2019 / Accepted: 10 September 2019 / Published: 19 September 2019
Previous studies have shown that soft computing models are excellent predictive models for demand management problems. However, their applications in solving water demand forecasting problems have been scantily reported. In this study, feedforward artificial neural networks (ANNs) and a support vector machine (SVM) were used to forecast water consumption. Two ANN models were trained using different algorithms: differential evolution (DE) and conjugate gradient (CG). The performance of these soft computing models was investigated with real-world data sets from the City of Ekurhuleni, South Africa, and compared with conventionally used exponential smoothing (ES) and multiple linear regression (MLR). The results obtained showed that the ANN model that was trained with DE performed better than the CG-trained ANN and other predictive models (SVM, ES and MLR). This observation further demonstrates the robustness of evolutionary computation techniques amongst soft computing techniques. View Full-Text
Keywords: artificial neural network; evolutionary algorithms; exponential smoothing; multiple linear regression; water demand forecasting artificial neural network; evolutionary algorithms; exponential smoothing; multiple linear regression; water demand forecasting
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Oyebode, O.; Ighravwe, D.E. Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques. Resources 2019, 8, 156.

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