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

Oluwaseun Oyebode and Desmond Eseoghene Ighravwe
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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

Resources, 2019, vol. 8, issue 3, 1-18

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.

Keywords: artificial neural network; evolutionary algorithms; exponential smoothing; multiple linear regression; water demand forecasting (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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