Forecasting of Reference Crop Evapotranspiration in Huang-Huai-Hai Plain Based on XGBoost Machine Learning Model and Numerical Weather Prediction Data
This study seeks to enhance the prediction accuracy of reference crop evapotranspiration(ET0)in the Huang-Huai-Hai Plain.Three models,namely BP,XGBoost,and CatBoost,were developed utilizing historical daily meteorological data from 2020-2021.These data included maximum air temperature(Tmax),minimum air temperature(Tmin),total solar radiation(Ra),sunshine hours(S),relative humidity(RH),and wind speed at a height of 2 m(U2)for training purposes.The models were tested and forecasted using daily numerical weather prediction data from Xinxiang city,located in northern Henan Province,for the year 2022.The forecasted results were compared with the ET0 data calculated using FAO-56 Penman-Monteith model.The results showed that Ra,Tmax,and Tmin were most correlated with ET0 among all factors,and therefore were considered as input factors for model running.The three models generated acceptable ET0 accuracy at 1-16 d forecast scale.In model testing,XGBoost model had R=0.875,RMSE=0.230 mm/d,MAE=0.181 mm/d,and MAPE=8.45％,respectively.On average,the R value of XGBoost model was increased by 10.2％,and values of RMSE,MAE,and MAPE were decreased by 39.9％-62.4％,compared to BP and CatBoost models.In view of the accuracy and stability of the XGBoost model,it can be a recommended model for ET0 forecasting in the Huang-Huai-Hai Plain.
万方数据
维普
