Open Access
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
1 School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang, 330099, China
2 Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling, 712100, China
3 Faculty of Agriculture and Food, Kunming University of Science and Technology, Kunming, 650500, China
* Corresponding Author: Lifeng Wu. Email:
Computer Modeling in Engineering & Sciences 2020, 125(2), 699-723. https://doi.org/10.32604/cmes.2020.011004
Received 14 April 2020; Accepted 24 July 2020; Issue published 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 (ET
0) in the next few months is
important for irrigation and reservoir management. Studies on forecasting
of multiple-month ahead ET
0 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 ET
0 (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 ET
0, 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 ET
0 forecasting in summer but use the GWO-XGB model in
other seasons.
Keywords
Cite This Article
APA Style
Lu, X., Fan, J., Wu, L., Dong, J. (2020). Forecasting multi-step ahead monthly reference evapotranspiration using hybrid extreme gradient boosting with grey wolf optimization algorithm. Computer Modeling in Engineering & Sciences, 125(2), 699-723. https://doi.org/10.32604/cmes.2020.011004
Vancouver Style
Lu X, Fan J, Wu L, Dong J. Forecasting multi-step ahead monthly reference evapotranspiration using hybrid extreme gradient boosting with grey wolf optimization algorithm. Comput Model Eng Sci. 2020;125(2):699-723 https://doi.org/10.32604/cmes.2020.011004
IEEE Style
X. Lu, J. Fan, L. Wu, and J. Dong "Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm," Comput. Model. Eng. Sci., vol. 125, no. 2, pp. 699-723. 2020. https://doi.org/10.32604/cmes.2020.011004
Citations