Quantifying wintertime O₃ and NO_x formation with relevance vector machines.

ABSTRACT: This paper uses a machine learning model called a relevance vector machine (RVM) to quantify ozone (O₃) and nitrogen oxides (NO_x) formation under wintertime conditions. Field study measurements were based on previous work described by Olson et al. (2019), where continuous measurements were reported from a wintertime field study in Utah. RVMs were formulated using either O₃ or nitrogen dioxide (NO₂) as the output variable. Values of the correlation coefficient (r²) between predicted and measured concentrations were 0.944 for O₃ and 0.931 for NO₂. RVMs are constructed from the observed measurements and result in sparse model formulations, meaning that only a subset of the data is used to approximate the entire datas

SUBMITTER: Olson DA 

PROVIDER: S-EPMC8353961 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature