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

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 model

SUBMITTER: Hsu CY 

PROVIDER: S-EPMC7579284 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature