Application of Spatio-Temporal Clustering For Predicting Ground-Level Ozone Pollution-Reference-Cited by-同舟云学术

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Application of Spatio-Temporal Clustering For Predicting Ground-Level Ozone Pollution

Published:2017 Issue: Volume: Page:153-167
ISSN:1867-2434
Container-title:Advances in Geocomputation
language:
Short-container-title:

Author:

Ahmadi Mahdi,Huang Yan,John Kuruvilla

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-22786-3_15

Reference26 articles.

1. Abdul-Wahab S, Bouhamra W, Ettouney H, Sowerby B, Crittenden BD (1996) Predicting ozone levels. Environ Sci Pollut Res 3:195–204

2. Ahmadi M, John K (2015) Statistical evaluation of the impact of shale gas activities on ozone pollution in North Texas. Sci Total Environ 536:457–467

3. Al-Alawi SM, Abdul-Wahab SA, Bakheit CS (2008) Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone. Environ Model Softw 23:396–403

4. Austin E, Zanobetti A, Coull B, Schwartz J, Gold DR, Koutrakis P (2014) Ozone trends and their relationship to characteristic weather patterns. J Expo Sci Environ Epidemiol 25:532–542

5. Bruno F, Cocchi D, Trivisano C (2004) Forecasting daily high ozone concentrations by classification trees. Environmetrics 15:141–153

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