Fitting and Prediction of City’s API Based on RBF and Application of CCRBF Model in Atmosphere Environmental Quality

Min Qing Gong; Ming Wei Sun; Ming Zhong Jin

doi:10.4028/www.scientific.net/AMR.534.314

Paper Titles

Dynamic Analysis on Change and System Simulation of Food Production Materials in the Northern Anhui Province
p.293

Research on Optimal Formula of Dustfall Agent to Remove Blasting Smoke
p.298

Synthesis of a New Silicon-Phosphorus Hybrid Flame Retardant from Waste Silicon Oil and its Application in Polypropylene System
p.304

Spectroscopic Investigation on OH (A-X) Emission in a Corona Radical Shower System for Purifying Flue Gas from Energy Material Burning
p.309

Fitting and Prediction of City’s API Based on RBF and Application of CCRBF Model in Atmosphere Environmental Quality
p.314

Environmental Pollution Analysis and Countermeasures of Chang-Ji Regional Integration with Analysis of Emission Materials from Waste Water and Pollutants
p.318

The Research of Low Carbon Decorative Materials
p.323

Study on the Treatment of Phosphate-Containing Waste Water by Modified Fly Ash
p.328

UV Induced Degradation of Crude Oil in Polluted Water Based on Analysis of Environmental Materials
p.333

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 534Fitting and Prediction of City’s API Based on RBF...

Fitting and Prediction of City’s API Based on RBF and Application of CCRBF Model in Atmosphere Environmental Quality

Abstract:

In this essay, the atmosphere environmental quality assessment of Guiyang was set as an example. A RBF model of the time series of API was built to fit and evaluate urban API on the basis of the concentration of 、 and inhalable particulate matter(PM10) in atmosphere. A CCRBF model used to test and predict atmosphere environmental quality was proposed by coupling canonical correlation analysis and RBF neural network based on various factors influencing urban atmosphere environmental quality. The analysis showed that the CCRBF simplified the quantity of factors effectively and was of higher fitting and prediction accuracies than regression model.

You might also be interested in these eBooks

Advanced Research on Material Science and Environmental Science

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 534)

Pages:

314-317

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.534.314

Citation:

Cite this paper

Online since:

June 2012

Authors:

Min Qing Gong, Ming Wei Sun, Ming Zhong Jin

Keywords:

API, Atmosphere Environmental Quality, CCA, Fitting & Forecast, RBF

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] G.Q. Tian: Research of Environmental Sciences, Vol. 9 (1996) No. 3, p.45. (In Chinese).

Google Scholar

[2] C.W. Zou and J.Q. Xiong: Sichuan Environment, Vol. 21 (2002) No. 3, p.17. (In Chinese).

Google Scholar

[3] X.H. Gan, N. Shi and B.G. Lin: Anhui Chemical Industry, Vol. 34 (2008) No. 5 p.59.

Google Scholar

[4] X.H. Yang and Z.F. Yang: ENVIR. ENGINEERING, Vol. 21 (2003) No. 5, p.69. (In Chinese).

Google Scholar

[5] Z.L. Dong, H.G. He and T. Yu: Journal of Arid Land Resources and Environment, Vol. 23 (2009) No. 12, p.49. (In Chinese).

Google Scholar

[6] D.Z. Li, Y.T. Li and F. Li: CONSTRUCTION & DESIGN FOR PROJECT, (2010) No. 7, p.74.

Google Scholar

[7] F.Y. Peng, Y. Ding and Y. Zhao: Research of Social and Water Conservation, Vol. 13 (2006) No. 5, p.154. (In Chinese).

Google Scholar

[8] Y.P. Chen and C.H. Zheng: ENVIRONMENTAL PROTECTION AND TECHNOLOGY, Vol. 14 (2008) No. 4, p.8. (In Chinese).

Google Scholar

[9] S.X. Li, J. Wen and X.H. Shao, JOURNAL OF HOHAI UNIVERSITY (NATURAL SCIENCES) Vol. 38 (2010) No. 2, p.149. (In Chinese).

Google Scholar

[10] Guiyang Stati. Bureau: Guiyang Statistical Yearbook (China Statistics Press, 2001—2009).

Google Scholar

[11] S. Haykin: NEURAL NETWORKS A Comprehension Foundation 2nd Edition (Tsinghua University Press, PR China, 2001).

Google Scholar