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Water Resources Management

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29-07-2022

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

Authors: Zhihao Xu, Zhiqiang Lv, Jianbo Li, Anshuo Shi

Published in: Water Resources Management | Issue 11/2022

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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.

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Title: A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning
Authors: Zhihao Xu
Zhiqiang Lv
Jianbo Li
Anshuo Shi
Publication date: 29-07-2022
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 11/2022
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-022-03255-5

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