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2014 | 23 | 2 |

Tytuł artykułu

Artificial neural networks for surface ozone prediction: models and analysis

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
Ozone is one of the most important constituents of the Earth's atmosphere. Ozone is vital because it maintains the thermal structure of the atmosphere. However, exposure to high concentrations of Ozone can cause serious problems to human health, vegetation, and damage to surfaces. The complexity of the relationship between the main attributes that severely affect surface ozone levels have made the problem of predicting its concentration very challenging. Innovative mathematical modeling techniques are urgently needed to get a better understanding of the dynamics of these attributes. In this paper, prediction of the surface ozone layer problem is investigated. A comparison between two types of artificial neural networks (ANN) (multilayer perceptron trained with backpropagation and radial basis functions (RBF) networks) for short prediction of surface ozone is conclusively demonstrated. Two models that predict the expected values of the surface ozone based on three variables (i.e. nitrogen-di-oxide, temperature, and relative humidity) are developed and compared.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

23

Numer

2

Opis fizyczny

p.341-348,fig.,ref.

Twórcy

autor
  • King Abdulla II School for Information Technology, University of Jordan, Amman, Jordan
  • Computer Science Department, Mutah University, Mutah, Jordan
autor
  • King Abdulla II School for Information Technology, University of Jordan, Amman, Jordan

Bibliografia

  • 1. SELVARAJ R., INBANATHAN S. R., MAHENDRAN O., JAYALAKSHMI R. Modelling of surface ozone using artificial neural network in an urban area. Int. J. Eng. Sci., 3, 2011.
  • 2. CHATTOPADHYAY S., BANDYOPADHYAY G. Artificial neural network with backpropagation learning to predict mean monthly total ozone in arosa, Switzerland, Int. J. Remote Sens., 28, (20), 4471, 2007.
  • 3. HÁJEK P., OLEJ V. Ozone prediction on the basis of neural networks, support vector regression and methods with uncertainty, Ecological Informatics, 12, (0), 31, 2012.
  • 4. ELKAMEL A., ABDUL-WAHAB S., BOUHAMRA W., ALPER E. Measurement and prediction of ozone levels around a heavily industrialized area: a neural network approach, Adv. Environ. Res., 5, (1), 47, 2001.
  • 5. WHO Health Aspects of Air Pollution with Particulate Matter, Ozone and Nitrogen Dioxide, Tech. Rep., WHO, 2003.
  • 6. FELZER B. S., CRONIN T., REILLY J. M., MELILLO J. M., WANG X. Impacts of ozone on trees and crops, Comptes Rendus Geoscience, 339, (11-12), 784, 2007.
  • 7. PASTOR-BÁRCENAS O., SORIA-OLIVAS E., MARTÍN-GUERRERO J. D., CAMPS-VALLS G., CARRASCO-RODRÍGUEZ J. L., DEL VALLE-TASCÓN S. Unbiased sensitivity analysis and pruning techniques in neural networks for surface ozone modelling, Ecol. Model., 182, (2), 149, 2005.
  • 8. AGIRRE E., ANTA A., BARRON L. J. R. Forecasting ozone levels using artificial neural networks, Forecasting Models, 2010.
  • 9. PRYBUTOK V. R., YI J., MITCHELL D. Comparison of neural network models with arima and regression models for prediction of houston’s daily maximum ozone concentrations, Eur. J. Oper. Res. 122, (1), 31, 2000.
  • 10. INBANATHAN S.S.R, MAHENDRAN, SELVARAJ R.S., JAYALAKSHMI R. Modelling of surface ozone using artificial neural network in an urban area. International Journal of Engineering Science and Technology., 3, (2), 1173, 2010.
  • 11. ABDUL-WAHAB S., AL-ALAWI S. Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks, Environ. Modell. Softw., 17, (3), 219, 2002.
  • 12. WANG W., LU W., WANG X., LEUNG A. Y. Prediction of maximum daily ozone level using combined neural network and statistical characteristics, Environ. Int., 29, (5), 555, 2003.
  • 13. SPELLMAN G. An application of artificial neural networks to the prediction of surface ozone concentrations in the united kingdom, Appl. Geogr., 19, (2), 123, 1999.
  • 14. COMAN A., IONESCU A., CANDAU Y. Hourly ozone prediction for a 24-h horizon using neural networks, Environ. Modell. Softw., 23, (12), 1407, 2008.
  • 15. CANU S., RAKOTOMAMONJY A. Ozone peak and pollution forecasting using support vectors, IFAC workshop on environmental modelling. International Federation of Automatic Control (IFAC): Yokohama, 2001.
  • 16. RYOKE M., NAKAMORI Y., HEYES C. A simplified ozone model based on fuzzy rules generation, Eur. J. Oper. Res., 122, 00, 2000.
  • 17. SELVARAJ R., GAYATHRI R., ELAMPARI K., JEYAKUMAR S. A neural network model for short term prediction of surface ozone at tropical city, Int. J. Eng. Sci., 2, 2010.
  • 18. HAYKIN S. Neural Networks: A Comprehensive Foundation. Upper Saddle River, NJ: Prentice Hall, 2nd Edition, 1999.
  • 19. KRSE B., VAN DER SMAGT P. An Introduction to Neural Networks. CRC Press, The University of Amsterdam, 1993.
  • 20. FAUSETT L. V. Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice Hall, 1994.
  • 21. SUNG A. Ranking importance of input parameters of neural networks, Expert Systems with Applications, 15, (3-4), 405, 1998.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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