國家衛生研究院 NHRI:Item 3990099045/12938
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    題名: Kriging-based land-use regression models that use machine learning algorithms to estimate the monthly BTEX concentration
    作者: Hsu, CY;Zeng, YT;Chen, YC;Chen, MJ;Lung, SCC;Wu, CD
    貢獻者: National Institute of Environmental Health Sciences
    摘要: This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial–temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms—random forest (RF) and extreme gradient boosting (XGBoost)—for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods.
    日期: 2020-09-23
    關聯: International Journal of Environmental Research and Public Health. 2020 Sep 23;17(19):Article number 6956.
    Link to: http://dx.doi.org/10.3390/ijerph17196956
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000586476700001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85091388143
    顯示於類別:[陳裕政] 期刊論文

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