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A Multi-imputation Method to Deal With Hydro-Meteorological Missing Values by Integrating Chain Equations and Random Forest

Author

Listed:
  • Xin Jing

    (Xi’an University of Technology)

  • Jungang Luo

    (Xi’an University of Technology)

  • Jingmin Wang

    (Project Construction Co.Ltd)

  • Ganggang Zuo

    (Xi’an University of Technology)

  • Na Wei

    (Xi’an University of Technology)

Abstract

Imputing hydro-meteorological missing values is essential in time series modeling. Imputation of missing values was traditionally performed after an observation period, which cannot effectively support water resources management in time. Therefore, it is necessary to deal with the missing data online. Moreover, traditional imputation methods usually consider only one observation variable and generate one set of imputations, which cannot describe the imputation uncertainty. Thus, a multiple-imputation method is proposed in this paper by integrating chain equations and random forest, namely, MICE-RF, to deal with the hydro-meteorological missing values. MICE-RF first simulates multiple imputation series to obtain the optimal imputations using the evaluation results of multiple imputation series. The traditional linear imputation, mean imputation, spline imputation, and k nearest neighbor imputation are compared to illustrate the robustness, reliability, and accuracy of the MICE-RF. According to the results, the MICE-RF provides the best imputation accuracy and can be easily implemented online.

Suggested Citation

  • Xin Jing & Jungang Luo & Jingmin Wang & Ganggang Zuo & Na Wei, 2022. "A Multi-imputation Method to Deal With Hydro-Meteorological Missing Values by Integrating Chain Equations and Random Forest," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1159-1173, March.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:4:d:10.1007_s11269-021-03037-5
    DOI: 10.1007/s11269-021-03037-5
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    References listed on IDEAS

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    1. Xinxin He & Jungang Luo & Ganggang Zuo & Jiancang Xie, 2019. "Daily Runoff Forecasting Using a Hybrid Model Based on Variational Mode Decomposition and Deep Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1571-1590, March.
    2. Hossein Bonakdari & Andrew D. Binns & Bahram Gharabaghi, 2020. "A Comparative Study of Linear Stochastic with Nonlinear Daily River Discharge Forecast Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3689-3708, September.
    3. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    4. Royston, Patrick & White, Ian R., 2011. "Multiple Imputation by Chained Equations (MICE): Implementation in Stata," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i04).
    5. Anas Mahmood Al-Juboori, 2019. "Generating Monthly Stream Flow Using Nearest River Data: Assessing Different Trees Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3257-3270, July.
    6. Wai Yan Lai & K. K. Kuok, 2019. "A Study on Bayesian Principal Component Analysis for Addressing Missing Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2615-2628, June.
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    Cited by:

    1. Chen, Fan & Yu, Lan & Mao, Jinqi & Yang, Qing & Wang, Delu & Yu, Chenghao, 2024. "A novel data-characteristic-driven modeling approach for imputing missing value in industrial statistics: A case study of China electricity statistics," Applied Energy, Elsevier, vol. 373(C).

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