A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning

Zhihao Xu, Zhiqiang Lv, Jianbo Li () and Anshuo Shi
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Zhihao Xu: Qingdao University
Zhiqiang Lv: Qingdao University
Jianbo Li: Qingdao University
Anshuo Shi: Qingdao University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 11, No 20, 4293-4312

Abstract: Abstract Predicting urban water demand is important in rationalizing water allocation and building smart cities. Influenced by multifarious factors, water demand is with high-frequency noise and complex patterns. It is difficult for a single learner to predict the nonlinear water demand time series. Therefore, ensemble learning is introduced in this work to predict water demand. A model (Word-embedded Temporal Feature Network, WE-TFN) for predicting water demand influenced by complex factors is proposed as a base learner. Besides, the seasonal time series model and the Principal Component Analysis and Temporal Convolutional Network (PCA-TCN) are combined with WE-TFN for ensemble learning. Based on the water demand data set provided by the Shenzhen Open Data Innovation Contest (SODIC), WE-TFN is compared with some typical models. The experimental results show that WE-TFN performs well in fitting local extreme values and predicting volatility. The ensemble learning method declines by approximately 68.73% on average on the Root Mean Square Error (RMSE) compared with a single base learner. Overall, WE-TFN and the ensemble learning method outperform baselines and perform well in water demand prediction.

Keywords: Multifarious factors; Time series; Base learner; Local extreme values; Volatility (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03255-5

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