Estimating Daily Dew Point Temperature Based on Local and Cross-StationMeteorological Data Using CatBoost Algorithm

作     者：Fuqi Yao Jinwei Sun Jianhua Dong 

作者机构：School of Hydraulic EngineeringLudong UniversityYantai264010China State Key Laboratory ofWater Resources and Hydropower Engineering ScienceWuhan UniversityWuhan430072China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2022年第130卷第2期

页      面：671-700页

核心收录：

学科分类：0711[理学-系统科学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the Shandong Provincial Natural Science Fund(ZR2020ME254 and ZR2020QD061) 

主　　题：Dew point temperature categorical boosting random forests cross-station accuracy 

摘      要：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.