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    ARTICLE

    Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm

    Xianghui Lu1, Junliang Fan2, Lifeng Wu1,*, Jianhua Dong3

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 699-723, 2020, DOI:10.32604/cmes.2020.011004 - 12 October 2020

    Abstract It is important for regional water resources management to know the agricultural water consumption information several months in advance. Forecasting reference evapotranspiration (ET0) in the next few months is important for irrigation and reservoir management. Studies on forecasting of multiple-month ahead ET0 using machine learning models have not been reported yet. Besides, machine learning models such as the XGBoost model has multiple parameters that need to be tuned, and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution. This study investigated the performance of the hybrid extreme… More >

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