Evaluating long-term and high spatiotemporal resolution of wet-bulb globe temperature through land-use based machine learning model.

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Bibliographic Details
Title: Evaluating long-term and high spatiotemporal resolution of wet-bulb globe temperature through land-use based machine learning model.
Authors: Hsu CY; Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, New Taipei City, Taiwan.; Center for Environmental Sustainability and Human Health, Ming Chi University of Technology, New Taipei City, Taiwan., Wong PY; Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan., Chern YR; Department of Geomatics, National Cheng Kung University, Tainan, Taiwan., Lung SC; Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan.; Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan., Wu CD; Department of Geomatics, National Cheng Kung University, Tainan, Taiwan. chidawu@mail.ncku.edu.tw.; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan. chidawu@mail.ncku.edu.tw.; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Tainan, Taiwan. chidawu@mail.ncku.edu.tw.
Source: Journal of exposure science & environmental epidemiology [J Expo Sci Environ Epidemiol] 2024 Nov; Vol. 34 (6), pp. 941-951. Date of Electronic Publication: 2023 Dec 16.
Publication Type: Journal Article
Journal Info: Publisher: Nature Pub. Group Country of Publication: United States NLM ID: 101262796 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1559-064X (Electronic) Linking ISSN: 15590631 NLM ISO Abbreviation: J Expo Sci Environ Epidemiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1559-064X
DOI:10.1038/s41370-023-00630-1