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

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  • Oluwaseun Oyebode

    (Centre for Research in Environmental, Coastal and Hydrological Engineering (CRECHE), Department of Civil Engineering, University of KwaZulu-Natal, Durban 4041, South Africa)

  • Desmond Eseoghene Ighravwe

    (Department of Mechanical and Biomedical Engineering, Bells University of Technology, Ota 112233, Nigeria)

Abstract

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.

Suggested Citation

  • Oluwaseun Oyebode & Desmond Eseoghene Ighravwe, 2019. "Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques," Resources, MDPI, vol. 8(3), pages 1-18, September.
    • Handle: RePEc:gam:jresou:v:8:y:2019:i:3:p:156-:d:268666
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    References listed on IDEAS

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    1. B. W. Wanjawa & L. Muchemi, 2014. "ANN Model to Predict Stock Prices at Stock Exchange Markets," Papers 1502.06434, arXiv.org.
    2. Unesco Unesco, 2015. "Water for a Sustainable World," Working Papers id:6657, eSocialSciences.
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    Cited by:

    1. Jun Guo & Hui Sun & Baigang Du, 2022. "Multivariable Time Series Forecasting for Urban Water Demand Based on Temporal Convolutional Network Combining Random Forest Feature Selection and Discrete Wavelet Transform," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3385-3400, July.

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