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Multivariable Time Series Forecasting for Urban Water Demand Based on Temporal Convolutional Network Combining Random Forest Feature Selection and Discrete Wavelet Transform

Author

Listed:
  • Jun Guo

    (Wuhan University of Technology
    Hubei Digital Manufacturing Key Laboratory)

  • Hui Sun

    (Wuhan University of Technology
    Hubei Digital Manufacturing Key Laboratory)

  • Baigang Du

    (Wuhan University of Technology
    Hubei Digital Manufacturing Key Laboratory)

Abstract

Urban water demand forecasting is crucial to reduce the waste of water resources and environmental protection. However, the non-stationarity and non-linearity of the water demand series under the influence of multivariate makes water demand prediction one of the long-standing challenges. This paper proposes a new hybrid forecasting model for urban water demand forecasting, which includes temporal convolution neural network (TCN), discrete wavelet transform (DWT) and random forest (RF). In order to improve the model’s forecasting abilities, the RF method is used to rank the factors and remove the less important factors. The dimension of raw data is reduced to improve calculating efficiency and accuracy. Then, the original water demand series is decomposed into different characteristic sub-series of multiple variables with better-behavior by DWT to weaken the fluctuation of original series. At the core of the proposed model, TCN is utilized to establish appropriate prediction models. Finally, to test and validate the proposed model, a real-world multivariate dataset from a water plant in Suzhou, China, is used for comparison experiments with the most recent state-of-the-art models. The results show that the mean absolute percentage error (MAPE) of the proposed model is 1.22% which is smaller than the other benchmark models. The proposed model indicates the only 2.2% of the prediction results have a relative error of more than 5%. It shows that the reliable results of the proposed model can be a superior tool for urban water demand forecasting.

Suggested Citation

  • Jun Guo & Hui Sun & Baigang Du, 2022. "Multivariable Time Series Forecasting for Urban Water Demand Based on Temporal Convolutional Network Combining Random Forest Feature Selection and Discrete Wavelet Transform," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3385-3400, July.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:9:d:10.1007_s11269-022-03207-z
    DOI: 10.1007/s11269-022-03207-z
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    References listed on IDEAS

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    1. Oluwaseun Oyebode & Desmond Eseoghene Ighravwe, 2019. "Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques," Resources, MDPI, vol. 8(3), pages 1-18, September.
    2. Suryanarayana, Gowri & Lago, Jesus & Geysen, Davy & Aleksiejuk, Piotr & Johansson, Christian, 2018. "Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods," Energy, Elsevier, vol. 157(C), pages 141-149.
    3. Haidong Huang & Zhixiong Zhang & Fengxuan Song, 2021. "An Ensemble-Learning-Based Method for Short-Term Water Demand Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1757-1773, April.
    4. Md Mahmudul Haque & Amaury Souza & Ataur Rahman, 2017. "Water Demand Modelling Using Independent Component Regression Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 299-312, January.
    5. Siddappa Pallavi & Shivamurthy Ravindra Yashas & Kotermane Mallikarjunappa Anilkumar & Behzad Shahmoradi & Harikaranahalli Puttaiah Shivaraju, 2021. "Comprehensive Understanding of Urban Water Supply Management: Towards Sustainable Water-socio-economic-health-environment Nexus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 315-336, January.
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    Cited by:

    1. Volkan Yilmaz & Mehmet Alpars, 2023. "An Investigation of the Temporal Interaction of Urban Water Consumption in the Framework of Settlement Characteristics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1619-1639, March.
    2. Jing Liu & Xin-Lei Zhou & Lu-Qi Zhang & Yue-Ping Xu, 2023. "Forecasting Short-term Water Demands with an Ensemble Deep Learning Model for a Water Supply System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2991-3012, June.

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