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
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
gradient boosting (XGBoost) model… More >
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