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A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming

Author

Listed:
  • Suning Liu

    (Southern University of Science and Technology
    Southern University of Science and Technology
    The University of Hong Kong)

  • Haiyun Shi

    (Southern University of Science and Technology
    Southern University of Science and Technology
    Qinghai University)

Abstract

Precipitation is regarded as the basic component of the global hydrological cycle. This study develops a recursive approach to long-term prediction of monthly precipitation using genetic programming (GP), taking the Three-River Headwaters Region (TRHR) in China as the study area. The daily precipitation data recorded at 29 meteorological stations during 1961–2014 are collected, among which the data during 1961–2000 are for calibration and the remaining data are for validation. To develop this approach, first, the preliminary estimations of annual precipitation are computed based on a statistical method. Second, the percentage of the monthly precipitation for each month of a year is calculated as the mean monthly precipitation divided by the mean annual precipitation during the study period, and then the preliminary estimation of monthly precipitation for each month of a year is obtained. Third, since GP can be used to improve the prediction results through establishing the relationship of the observations with the preliminary estimations at the past and current times, it is adopted to improve the preliminary estimations. The calibration and validation results reveal that the recursive approach involving GP can provide the more accurate predictions of monthly precipitation. Finally, this approach is used to predict the monthly precipitation over the TRHR till 2050. Overall, the proposed method and the obtained results will enhance our understanding and facilitate future studies regarding the long-term prediction of precipitation in such regions.

Suggested Citation

  • Suning Liu & Haiyun Shi, 2019. "A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1103-1121, February.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:3:d:10.1007_s11269-018-2169-0
    DOI: 10.1007/s11269-018-2169-0
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    References listed on IDEAS

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    Cited by:

    1. Tonglin Fu & Xinrong Li, 2020. "A Combination Forecasting Strategy for Precipitation, Temperature and Wind Speed in the Southeastern Margin of the Tengger Desert," Sustainability, MDPI, vol. 12(4), pages 1-22, February.
    2. Bin Xu & Xin Huang & Ping-an Zhong & Yenan Wu, 2020. "Two-Phase Risk Hedging Rules for Informing Conservation of Flood Resources in Reservoir Operation Considering Inflow Forecast Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2731-2752, July.

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