TY - EJOU AU - Lu, Xianghui AU - Fan, Junliang AU - Wu, Lifeng AU - Dong, Jianhua TI - Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm T2 - Computer Modeling in Engineering \& Sciences PY - 2020 VL - 125 IS - 2 SN - 1526-1506 AB - 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. KW - Reference evapotranspiration; extreme gradient boosting; Grey Wolf Optimizer; multi-layer perceptron; M5 model tree DO - 10.32604/cmes.2020.011004