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Hydrology 2018, 5(4), 54; https://doi.org/10.3390/hydrology5040054

Short-Term Water Demand Forecasting Model Combining Variational Mode Decomposition and Extreme Learning Machine

Department of Constructional and Environmental Engineering, Kyungpook National University, Sangju 37224, Korea
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Received: 14 August 2018 / Revised: 19 September 2018 / Accepted: 25 September 2018 / Published: 27 September 2018
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Abstract

Accurate water demand forecasting is essential to operate urban water supply facilities efficiently and ensure water demands for urban residents. This study proposes an extreme learning machine (ELM) coupled with variational mode decomposition (VMD) for short-term water demand forecasting in six cities (Anseong-si, Hwaseong-si, Pyeongtaek-si, Osan-si, Suwon-si, and Yongin-si), South Korea. The performance of VMD-ELM model is investigated based on performance indices and graphical analysis and compared with that of artificial neural network (ANN), ELM, and VMD-ANN models. VMD is employed for multi-scale time series decomposition and ANN and ELM models are used for sub-time series forecasting. As a result, ELM model outperforms ANN model. VMD-ANN and VMD-ELM models outperform ANN and ELM models, and the VMD-ELM model produces the best performance among all the models. The results obtained from this study reveal that the coupling of VMD and ELM can be an effective forecasting tool for short-term water demands with strong nonlinearity and non-stationarity and contribute to operating urban water supply facilities efficiently. View Full-Text
Keywords: extreme learning machine; variational mode decomposition; water demand forecasting; artificial neural network extreme learning machine; variational mode decomposition; water demand forecasting; artificial neural network
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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).
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Seo, Y.; Kwon, S.; Choi, Y. Short-Term Water Demand Forecasting Model Combining Variational Mode Decomposition and Extreme Learning Machine. Hydrology 2018, 5, 54.

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