| Cite this article: | 刘小强,代智光,吴立峰.GPR、XGBoost和CatBoost模拟江西地区参考作物蒸散量的适应性研究[J].灌溉排水学报,0,():-. |
| Liu Xiaoqiang,Daizhiguang,Wulifeng.GPR、XGBoost和CatBoost模拟江西地区参考作物蒸散量的适应性研究[J].灌溉排水学报,0,():-. |
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| Study on adaptability of GPR,XGBoost and CatBoost to simulate reference crop evaporation in Jiangxi Province |
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Liu Xiaoqiang, Daizhiguang, Wulifeng
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Nanchang Institute of Technology
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| Abstract: |
| 【Background】The rapid turn of drought and waterlogging in Jiangxi need a reasonable irrigation regime to ensure the steady production of crops.【Objective】In order to improve the adaptability and accuracy of machine learning model simulation reference crop evapotranspiration in Jiangxi.【Method】meteorological data from 2001 to 2015 at 15 stations of Jiangxi（i.e.,daily maximum and minimum ambient temperature，global solar radiation，extra-terrestrial solar radiation, relative humidity and 2m high wind speed）was used to train and text the proposed model of Gaussian process regression、 extreme gradient enhancement and gradient-enhanced decision tree.【Result】The results show thatthe influence of each meteorological element on the accuracy of machine learning model simulation ET0 from large to small is: Rs, Tmax and Tmin, RH, U2; the machine learning model with combination of Tmax, Tmin, Rs and U2 meteorological elements（RMSE<0.2mm/d）obtains the high ET0accuracy. In addition, three machine learning models have good applicability in limited meteorological data, and are superior to the traditional empirical model, The prediction accuracy of GPR and CatBoost model is high, but the stability of GPR model is good.【Conclusion】 In view of the complexity, accuracy and stability of the model, the GPR model can be used as a recommended method for the simulation of evapotranspiration in Jiangxi province. |
| Key words: reference crop evapotranspiration; gaussian process regression; extreme gradient boosting; gradient boosting with categorical features support; empirical model |
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