Abstract
Estimation of Particulate Matter concentration (PM1, PM2.5 and PM10) from aerosol product derived from satellite images and meteorological parameters brings a great advantage in air pollution monitoring since observation range is no longer limited around ground stations and estimation accuracy will be increased significantly. In this article, we investigate the application of Multiple Linear Regression (MLR) and Support Vector Regression (SVR) to make empirical data models for PM1/2.5/10 estimation from satellite- and ground-based data. Experiments, which are carried out on data recorded in two year over Hanoi - Vietnam, not only indicate a case study of regional modeling but also present comparison of performance between a widely used technique (MLR) and an advanced method (SVR).
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Nguyen, T.N.T., Ta, V.C., Le, T.H., Mantovani, S. (2014). Particulate Matter Concentration Estimation from Satellite Aerosol and Meteorological Parameters: Data-Driven Approaches. In: Huynh, V., Denoeux, T., Tran, D., Le, A., Pham, S. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 244. Springer, Cham. https://doi.org/10.1007/978-3-319-02741-8_30
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DOI: https://doi.org/10.1007/978-3-319-02741-8_30
Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-02741-8
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