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| Title | A Novel Approach For Predicting Water Demand With Complex Patterns Based On Ensemble Learning |
|---|---|
| ID_Doc | 3229 |
| Authors | Xu Z.; Lv Z.; Li J.; Shi A. |
| Year | 2022 |
| Published | Water Resources Management, 36, 11 |
| DOI | http://dx.doi.org/10.1007/s11269-022-03255-5 |
| 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. © 2022, The Author(s), under exclusive licence to Springer Nature B.V. |
| Author Keywords | Base learner; Local extreme values; Multifarious factors; Time series; Volatility |
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