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

Author

Listed:
  • Zhihao Xu

    (Qingdao University
    Qingdao University
    Chinese Academy of Sciences)

  • Zhiqiang Lv

    (Qingdao University
    Qingdao University
    Chinese Academy of Sciences)

  • Jianbo Li

    (Qingdao University
    Qingdao University)

  • Anshuo Shi

    (Qingdao University
    Qingdao University)

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.

Suggested Citation

  • Zhihao Xu & Zhiqiang Lv & Jianbo Li & Anshuo Shi, 2022. "A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4293-4312, September.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:11:d:10.1007_s11269-022-03255-5
    DOI: 10.1007/s11269-022-03255-5
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    References listed on IDEAS

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    1. Mukand Babel & Nisuchcha Maporn & Victor Shinde, 2014. "Incorporating Future Climatic and Socioeconomic Variables in Water Demand Forecasting: A Case Study in Bangkok," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 2049-2062, May.
    2. Salih Muhammad Awadh & Heba Al-Mimar & Zaher Mundher Yaseen, 2021. "Groundwater availability and water demand sustainability over the upper mega aquifers of Arabian Peninsula and west region of Iraq," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 1-21, January.
    3. Shan Zou & Abuduwaili Jilili & Weili Duan & Philippe De Maeyer & Tim Van de Voorde, 2019. "Human and Natural Impacts on the Water Resources in the Syr Darya River Basin, Central Asia," Sustainability, MDPI, vol. 11(11), pages 1-18, May.
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    Cited by:

    1. Ke Wang & Zanting Ye & Zhangquan Wang & Banteng Liu & Tianheng Feng, 2023. "MACLA-LSTM: A Novel Approach for Forecasting Water Demand," Sustainability, MDPI, vol. 15(4), pages 1-19, February.

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