Short-term water demand forecasting model combining variational mode decomposition and extreme learning machine

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.