Jingxin Yu1,3, Wengang Zheng1,*, Linlin Xu3, Lili Zhangzhong1, Geng Zhang2, Feifei Shan1
Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 989-1003, 2020, DOI: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 More >
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