Fuqi Yao1, Jinwei Sun1, Jianhua Dong2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 671-700, 2022, DOI:10.32604/cmes.2022.018450
Abstract Accurate estimation of dew point temperature (Tdew) plays a very important role in the fields of water resource management, agricultural engineering, climatology and energy utilization. However, there are few studies on the applicability of local Tdew algorithms at regional scales. This study evaluated the performance of a new machine learning algorithm, i.e., gradient boosting on decision trees with categorical features support (CatBoost) to estimate daily Tdew using limited local and cross-station meteorological data. The random forests (RF) algorithm was also assessed for comparison. Daily meteorological data from 2016 to 2019, including maximum, minimum and average temperature (Tmax, Tmin and Tmean),… More >
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