國家衛生研究院 NHRI:Item 3990099045/15559
English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 12500/13673 (91%)
造訪人次 : 2631660      線上人數 : 281
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋
    主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於NHRI ‧ 管理 到手機版


    請使用永久網址來引用或連結此文件: http://ir.nhri.org.tw/handle/3990099045/15559


    題名: Evaluating long-term and high spatiotemporal resolution of wet-bulb globe temperature through land-use based machine learning model
    作者: Hsu, CY;Wong, PY;Chern, YR;Lung, SCC;Wu, CD
    貢獻者: National Institute of Environmental Health Sciences
    摘要: Background: The increase in global temperature and urban warming has led to the exacerbation of heatwaves, which negatively affect human health and cause long-term loss of work productivity. Therefore, a global assessment in temperature variation is essential. Objective: This paper is the first of its kind to propose land-use based spatial machine learning (LBSM) models for predicting highly spatial-temporal variations of wet-bulb globe temperature (WBGT), which is a heat stress indicator used to assess thermal comfort in indoor and outdoor environments, specifically for the main island of Taiwan. Methods: To develop spatiotemporal prediction models for both the working period and noon period, we calculated the WBGT of each weather station from 2001 to 2019 using temperature, humidity, and solar radiation data. These WBGT estimations were then used as the dependent variable for developing the spatiotemporal prediction models. To enhance model performance, we used innovative approaches that combined SHapley Additive exPlanations (SHAP) values for the selection of non-linear variables, along with machine learning algorithms for model development. Results: When incorporating temperature along with other land-use/land cover predictor variables, the performance of LBSM models was excellent, with an R2 value of up to 0.99. The LBSM models explained 98% and 99% of the spatial-temporal variations in WBGT for the working and noon periods, respectively, within the complete models. In the temperature-excluded models, the explained variances were 94% and 96% for the working and noon periods, respectively. Impact: WBGT is a common method used by many organizations to access the impact of heat stress on human beings. However, limited studies have mentioned the association between WBGT and health impacts due to the absence of spatiotemporal databases. This study develops a new approach using land-use-based spatial machine learning (LBSM) models to better predict the fine spatial-temporal WBGT levels, with a 50-m × 50-m grid resolution for both working time and noontime. Our proposed methodology could be used in future studies aimed at evaluating the potential long-term loss of work productivity due to the effects of global warming or urban heat island.
    日期: 2024-11
    關聯: Journal of Exposure Science and Environmental Epidemiology. 2024 Nov;34(6):941-951.
    Link to: http://dx.doi.org/10.1038/s41370-023-00630-1
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1559-0631&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:001129938500001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85179747313
    顯示於類別:[其他] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    SCP85179747313.pdf2018KbAdobe PDF124檢視/開啟


    在NHRI中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋