Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States

Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States

Background: Spatial linear Land-Use Regression (LUR) is commonly used for long-term modeling of air pollution in support of exposure and epidemiological assessments. Machine Learning (ML) methods in conjunction with spatiotemporal modeling can provide more flexible exposure-relevant metrics and have...

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Bibliographic Details
Main Authors: Xiang Ren, Zhongyuan Mi, Panos G. Georgopoulos
Format: Article
Language:English
Published: Elsevier 2020-09-01
Series:Environment International
Subjects:
Machine learning
Land use regression
Ozone
Spatiotemporal modeling
Black-box model interpretation
Online Access:http://www.sciencedirect.com/science/article/pii/S0160412020317827

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http://www.sciencedirect.com/science/article/pii/S0160412020317827

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