Open Access
ARTICLE
A PSO-XGBoost Model for Estimating Daily Reference Evapotranspiration in the Solar Greenhouse
Jingxin Yu1,3, Wengang Zheng1,*, Linlin Xu3, Lili Zhangzhong1, Geng Zhang2, Feifei Shan1
1 National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
2 National Agro-tech Extension and Service Center, Beijing, 100125, China
3 School of Land Science and Technology, China University of Geosciences, Beijing, 100083, China
* Corresponding Author: Wengang Zheng. Email:
Intelligent Automation & Soft Computing 2020, 26(5), 989-1003. https://doi.org/10.32604/iasc.2020.010130
Abstract
Accurate estimation of reference evapotranspiration (ET0) is a critical
prerequisite for the development of agricultural water management strategies. It is
challenging to estimate the ET0 of a solar greenhouse because of its unique
environmental variations. Based on the idea of ensemble learning, this paper
proposed a novel ET0i estimation model named PSO-XGBoost, which took
eXtreme Gradient Boosting (XGBoost) as the main regression model and used
Particle Swarm Optimization (PSO) algorithm to optimize the parameters of
XGBoost. Using the meteorological and soil moisture data during the two-crop
planting process as the experimental data, and taking ET0i calculated based on the
improved Penman–Monteith equation as the reference truth, the accuracy of model
estimation was evaluated and the impact of less input variables on model
estimation was tested. The results showed that PSO algorithm could optimize the
parameters of XGBoost model stably, PSO-XGBoost model could accurately
estimate ET0i in various data modes, and the estimation accuracy of the model
decreases with the decrease of the number of input variables. Compared with other
integrated learning models, PSO-XGBoost model could obtain the best estimation
performance of ET
0i.
Keywords
Cite This Article
J. Yu, W. Zheng, L. Xu, L. Zhangzhong, G. Zhang
et al., "A pso-xgboost model for estimating daily reference evapotranspiration in the solar greenhouse,"
Intelligent Automation & Soft Computing, vol. 26, no.5, pp. 989–1003, 2020.