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Estimating Daily Dew Point Temperature Based on Local and cross-stationMeteorological Data Using CatBoost Algorithm

Computer Modeling in Engineering & Sciences 2022年第2期130卷 671-700页

作者： Fuqi Yao Jinwei Sun Jianhua Dong School of Hydraulic Engineering Ludong UniversityYantai264010China State Key Laboratory ofWater Resources and Hydropower Engineering Science Wuhan UniversityWuhan430072China

Accurate estimation of dew point temperature(Tdew)plays a very important role in the fields of water resource management,agricultural engineering,climatology and energy ***,there are few studies on the applicability of local Tdew algorithms at regional *** study evaluated the performance of a new machine learning algorithm,i.e.,gradient boosting on decision trees with categorical features support(Cat Boost)to estimate daily Tdew using limited local and cross-station meteorological *** random forests(RF)algorithm was also assessed for *** meteorological data from 2016 to 2019,including maximum,minimum and average temperature(Tmax,Tmin and Tmean),maximum,minimum and average relative humidity(RHmax,RHmin and RHmean),maximum,minimum and average global solar radiation(Rsmax,Rsmin and Rsmean)from three weather stations in Hunan of China were used to evaluate the CatBoost and RF *** results showed that both algorithms achieved satisfactory estimation accuracy at the target stations(on average RMSE=1.020℃,R^(2)=0.969,MAE=0.718℃and NRMSE=0.087)in the absence of complete meteorological parameters(with only temperature data as input).The Cat Boost algorithm(on average RMSE=1.900℃and R^(2)=0.835)was better than the RF algorithm(on average RMSE=2.214℃andR^(2)=0.828).The accuracy and stability of the CatBoost and RF algorithms were positively correlated with the number of input parameters,and the three-parameter algorithms achieved higher estimation accuracy than the two-parameter *** developed methodology is helpful to predict Tdew at regional scale.

关键词： Dew point temperature categorical boosting random forests cross-station accuracy

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