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Estimating the spatiotemporal variation of NO2 concentration using an adaptive neuro-fuzzy inference system

journal contribution
posted on 10.10.2018, 00:00 by B Yageneh, Michael Hewson, S Clifford, A Tavassoli, LD Knibbs, L Morawska
Statistical modelling has been successfully used to estimate the variations of NO2 concentration, but employing new modelling techniques can make these estimations far more accurate. To do so, for the first time in application to spatiotemporal air pollution modelling, we employed a soft computing algorithm called adaptive neuro-fuzzy inference system (ANFIS) to estimate the NO2 variations. Comprehensive data sets were investigated to determine the most effective predictors for the modelling process, including land use, meteorological, satellite, and traffic variables. We have demonstrated that using selected satellite, traffic, meteorological, and land use predictors in modelling increased the R2 by 21%, and decreased the root mean square error (RMSE) by 47% compared with the model only trained by land use and meteorological predictors. The ANFIS model found to have better performance and higher accuracy than the multiple regression model. Our best model, captures 91% of the spatiotemporal variability of monthly mean NO2 concentrations at 1 km spatial resolution (RMSE 1.49 ppb) in a selected area of Australia.

Funding

Category 3 - Industry and Other Research Income

History

Volume

100

Start Page

222

End Page

235

Number of Pages

14

eISSN

1873-6726

ISSN

1364-8152

Publisher

Pergamon Press, UK

Peer Reviewed

Yes

Open Access

No

Acceptance Date

20/11/2017

External Author Affiliations

The University of Queensland; Centre for Air Quality and Health Research and Evaluation, NSW; Queensland University of TechnoloY

Era Eligible

Yes

Journal

Environmental Modelling and Software

Exports

CQUniversity

Exports