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Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

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  • Xin Liu

    (North China University of Water Resources and Electric Power
    Research Office for Water Resources Management, China Institute of Water Resources and Hydropower Research)

  • Xuefeng Sang

    (Research Office for Water Resources Management, China Institute of Water Resources and Hydropower Research)

  • Jiaxuan Chang

    (Research Office for Water Resources Management, China Institute of Water Resources and Hydropower Research)

  • Yang Zheng

    (Research Office for Water Resources Management, China Institute of Water Resources and Hydropower Research)

Abstract

The water supply in megacities can be affected by the living habits and population mobility, so the fluctuation degree of daily water supply data is acute, which presents a great challenge to the water demand prediction. This is because that non-stationarity of daily data can have a large influence on the generalization ability of models. In this study, the Hodrick-Prescott (HP) and wavelet transform (WT) methods were used to carry out decomposition of daily data to solve the non-stationarity problem. The bidirectional long short term memory (BLSTM), seasonal autoregressive integrated moving average (SARIMA) and Gaussian radial basis function neural network (GRBFNN) were developed to carry out prediction of different subseries. The ensemble learning was introduced to improve the generalization ability of models, and prediction interval was generated based on student's t-test to cope with the variation of water supply laws. This study method was applied to the daily water demand prediction in Shenzhen and cross-validation was performed. The results show that WT is superior to HP decomposition method, but maximum decomposition level of WT should not be set too high, otherwise the trend characteristics of subseries will be weakened. Although the corona virus disease 2019 (COVID-19) outbreak caused a variation in water supply laws, this variation is still within the prediction interval. The WT and coupling models accurately predict water demand and provide the optimal mean square error (0.17%), Nash-Sutcliffe efficiency (97.21%), mean relative error (0.1), mean absolute error (3.32%), and correlation coefficient (0.99).

Suggested Citation

  • Xin Liu & Xuefeng Sang & Jiaxuan Chang & Yang Zheng, 2021. "Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4021-4041, September.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:12:d:10.1007_s11269-021-02927-y
    DOI: 10.1007/s11269-021-02927-y
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    References listed on IDEAS

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    1. Yongtao Wang & Jian Liu & Rong Li & Xinyu Suo & EnHui Lu, 2022. "Medium and Long-term Precipitation Prediction Using Wavelet Decomposition-prediction-reconstruction Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 971-987, February.

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