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.
Highlights
The United Nations’ (UN) Vision 2050 aims to ensure that enough and safe water is made available to meet every person’s basic needs, with healthy lifestyles and behaviors upheld through reliable and affordable water supply and sanitation services [1]
To address the above-mentioned knowledge gaps, this study investigated the potential of two soft computing techniques (ANN and support vector machine (SVM)) as predictive models for municipal water demand forecasting
This study compared the performance of soft computing techniques (ANN–conjugate gradient (CG), artificial neural networks (ANNs)–differential evolution (DE) and SVM) with exponential smoothing (ES) and multiple linear regression (MLR) as predictive models for water consumption
Summary
The United Nations’ (UN) Vision 2050 aims to ensure that enough and safe water is made available to meet every person’s basic needs, with healthy lifestyles and behaviors upheld through reliable and affordable water supply and sanitation services [1]. To this end, in its World Water Development. Report (2015), it identified the validating and tailoring of data for water management decision-making systems as one of the outstanding challenges to be met in knowledge generation and policy formulation To solve this problem, water forecasting models are required to make water management policies more efficient [2]. Long-term forecasts are imperative for planning and infrastructure design, for instance, in providing new water supplies and upgrading the capacity of existing water treatment plants, while short-term forecasts provide guidance in operating and managing water resources and associated
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