Predicting Ozone Layer Concentration Using Multivariate Adaptive Regression Splines, Random Forest and Classification and Regression Tree-Reference-Cited by-同舟云学术

Predicting Ozone Layer Concentration Using Multivariate Adaptive Regression Splines, Random Forest and Classification and Regression Tree

Published:2017-10-04 Issue: Volume: Page:140-152
ISSN:2194-5357
Container-title:Soft Computing Applications
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Author:

Roy Sanjiban Sekhar,Pratyush Chitransh,Barna Cornel

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-62524-9_11

Reference25 articles.

1. Ozer, P., Laghdaf, M.B.O.M., Lemine, S.O.M., Gassan, J.: Estimation of air quality degradation due to Saharan dust at Nouakchott, Mauritania, from horizontal visibility data. Water Air Soil Pollut. 178(1–4), 79 (2007)

2. Zhang, W.Y., Han, T.T., Zhao, Z.B., Zhang, J., Wang, Y.F.: The prediction of surface layer ozone concentration using an improved AR Model. In: International Conference of Information Technology, Computer Engineering and Management Sciences, Nanjing, Jiangsu, pp. 72–75 (2011)

3. Assessment for Decision-Makers: Scientific Assessment of Ozone Depletion: 2014, World Meteorological Organization, Global Ozone Research and Monitoring Project—Report No. 56, Geneva, Switzerland (2014)

4. Zhang, C., Yuan, D.: Fast fine-grained air quality index level prediction using random forest algorithm on cluster computing of spark. In: 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing and 2015 IEEE 12th International Conference on Autonomic and Trusted Computing and 2015 IEEE 15th International Conference on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), Beijing, pp. 929–934 (2015)

5. Wang, Y., Yue, W.: Target data association in communication constrained environment using CART: compressed adaptive reference topology. In: 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), Beijing, China, pp. 333–338 (2016)

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