GPR、XGBoost和CatBoost模拟江西地区参考作物蒸散量的适应性研究

目的]提高机器学习模型模拟参考作物蒸散量在江西省适应性和精度.方法]基于江西南昌等15个气象站2001—2015年日值气象数据(最高气温、最低气温、地表辐射、大气顶层辐射、相对湿度和2 m高风速),以FAO-56 Penman-Monteith(P-M)公式的计算结果作为对照,建立了计算ET0的高斯过程回归(GPR...

Comparing the Performance of GPR,XGBoost and CatBoost Models for Calculating Reference Crop Evapotranspiration in Jiangxi Province

【Background】Alternate drought and waterlogging increasingly occurring in Jiangxi province means that rational irrigation strategies are required to safeguard its agricultural production.【Objective】The objective of this paper is to select a suitable machine learning model to calculate reference crop evapotranspiration across the province.【Method】Meteorological data-including daily maximum(Tmax) and minimum(Tmin) ambient temperature,global solar radiation, extra-terrestrial solar radiation(Rs), relative humidity(RH) and 2 m-height wind speed(U2)-were measured from 2001 to 2015 at 15 stations across the province; they were then used to train and test three models: The gaussian process regression(GPR), the extreme gradient boosting(XGBoost), and the gradient boosting with categorical features support(CatBoost). We compared accuracy with empirical model for estimating the reference evapotranspiration.【Result】The meteorological factors that impacted the accuracy of the machine learning model for estimating ET0 was ranked in the descending order as follows based on their significance: Rs>Tmax>Tmin>RH>U2. Models using Tmax, Tmin, Rs and U2 gave the most accurate ET0 estimate with RMSE<0.2 mm/d. All three models have a good applicability by using limited meteorological data, and are superior to the traditional empirical model. In particular, GPR and CatBoost were more accurate, and GPR was most stable.【Conclusion】In terms of complexity, accuracy and stability, GPR was the most suitable model for estimating reference crop evapotranspiration in Jiangxi province.