Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration

Chin-Yu Hsu, Yu-Ting Zeng, Yu-Cheng Chen, Mu-Jean Chen, Shih-Chun Candice Lung and Chih-Da Wu
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Chin-Yu Hsu: Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, New Taipei 243303, Taiwan
Yu-Ting Zeng: Department of Geomatics, National Cheng Kung University, Tainan 70101, Taiwan
Yu-Cheng Chen: National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli 35053, Taiwan
Mu-Jean Chen: National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli 35053, Taiwan
Shih-Chun Candice Lung: Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
Chih-Da Wu: Department of Geomatics, National Cheng Kung University, Tainan 70101, Taiwan

IJERPH, 2020, vol. 17, issue 19, 1-14

Abstract: 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.

Keywords: nitrogen dioxide (NO 2 ); hybrid Kriging-LUR model; culture-specific sources; spatiotemporal variations (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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