An Ensemble-Learning-Based Method for Short-Term Water Demand Forecasting

Haidong Huang (), Zhixiong Zhang () and Fengxuan Song ()
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Haidong Huang: Beibu Gulf University
Zhixiong Zhang: Beibu Gulf University
Fengxuan Song: Beibu Gulf University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 6, No 6, 1757-1773

Abstract: Abstract Short-term water demand forecasting has always been a hot research topic in the field of water distribution systems, and many researchers have developed a wide variety of methods based on different prediction periodicities. However, few studies have paid attention to using ensemble learning methods for short-term water demand forecasting. In this study, an ensemble-learning-based method was developed to forecast short-term water demand. The proposed method consists of two models: an equal-dimension and new-information model and an ensemble learning model. The purpose of the equal-dimension and new-information model is to update data for modelling periodically, while the ensemble learning model is used for water demand forecasting. To evaluate the forecasting performance, the proposed method was applied to a data set obtained from a real-world water distribution system and compared with the single back-propagation neural network (BPNN) model and the seasonal autoregressive integrated moving average (SARIMA) model. The results show that the proposed method improves both the accuracy and stability of water demand prediction. The proposed method has the potential to provide a promising alternative for short-term water demand forecasting.

Keywords: Ensemble learning; Water demand forecasting; Short-term; Adaptive boosting algorithm (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11269-021-02808-4

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