@Article{iasc.2020.010130,
AUTHOR = {Jingxin Yu, Wengang Zheng, Linlin Xu, Lili Zhangzhong, Geng Zhang, Feifei Shan},
TITLE = {A PSO-XGBoost Model for Estimating Daily Reference Evapotranspiration in  the Solar Greenhouse},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {5},
PAGES = {989--1003},
URL = {http://www.techscience.com/iasc/v26n5/40819},
ISSN = {2326-005X},
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<sub>0i</sub>.},
DOI = {10.32604/iasc.2020.010130}
}