@Article{cmes.2020.011004,
AUTHOR = {Xianghui Lu, Junliang Fan, Lifeng Wu, Jianhua Dong},
TITLE = {Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {125},
YEAR = {2020},
NUMBER = {2},
PAGES = {699--723},
URL = {http://www.techscience.com/CMES/v125n2/40314},
ISSN = {1526-1506},
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 coupled with the Grey Wolf Optimizer
(GWO) algorithm for forecasting multi-step ahead ET0 (1–3 months ahead),
compared with three conventional machine learning models, i.e., standalone
XGBoost, multi-layer perceptron (MLP) and M5 model tree (M5) models in
the subtropical zone of China. The results showed that the GWO-XGB model
generally performed better than the other three machine learning models in
forecasting 1–3 months ahead ET0, followed by the XGB, M5 and MLP
models with very small differences among the three models. The GWO-XGB
model performed best in autumn, while the MLP model performed slightly
better than the other three models in summer. It is thus suggested to apply the
MLP model for ET0 forecasting in summer but use the GWO-XGB model in
other seasons.},
DOI = {10.32604/cmes.2020.011004}
}