Downscaling the Midsummer Temperature-Humidity Index Based on Multiple Machine Learning Methods

Downscaling the Midsummer Temperature-Humidity Index Based on Multiple Machine Learning Methods

To improve the finesse of the temperature-humidity index (THI), this study applies four machine learning methods in THI downscaling, including multiple linear regression, random forest (RF), support vector machine, and gradient boosting machine. The temperature data and specific humidity data of the...

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Bibliographic Details
Main Authors: Danwa Wu, Zhenhai Yao, Linlin Wu, Xichang Luo, Shuai Sun, Binfang He, Yali Zhang
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
China meteorological administration land data assimilation system (CLDAS)
downscale
machine learning
moderate-resolution imaging spectroradiometer (MODIS)
temperature-humidity index (THI)
Online Access:https://ieeexplore.ieee.org/document/10195986/

Internet

https://ieeexplore.ieee.org/document/10195986/

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