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    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

    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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