Air Quality, Atmosphere & Health

, Volume 11, Issue 5, pp 559–569 | Cite as

Cycle reservoir with regular jumps for forecasting ozone concentrations: two real cases from the east of Croatia

CRJ for forecasting ozone concentrations
  • Alaa Sheta
  • Hossam Faris
  • Ali Rodan
  • Elvira Kovač-Andrić
  • Ala’ M. Al-Zoubi
Article

Abstract

Satisfying the national air quality standards represents a challenge nowadays for developing countries. Air pollution in industrial cities is one of the foremost problems that affect human health and might cause loss of human life. One of the main attributes that can cause a significant impact on people’s health is the ground-level ozone pollution. Ozone can raise the ratio of asthma attacks, permanent damage to lungs, and maybe death. Forecasting its concentration levels is essential for planning well-designed environment protection strategies. In this paper, a state-space reservoir model called cycle reservoir with jumps (CRJ) is used to predict the level of ozone concentrations in the east of Croatia utilizing some meteorological parameters including the temperature, relative humidity, wind speed, wind direction, and the pollutants PM10. CRJ is a particular type of recurrent neural networks with powerful performance when applied for complex temporal problems. Two cases from the east of Croatia are investigated in this work: the Kopaćki Rit area and the Osijek city. The proposed CRJ model shows superiority of CRJ model in forecasting ozone concentrations compared to linear regression, multilayer perceptron (MLP) and radial basis function (RBF) network.

Keywords

Neural networks Reservoirs Ozone prediction CRJ Croatia 

Notes

Acknowledgements

The authors gratefully acknowledge the financial support given to the project by the Croatian Ministry of Science, Education and Sports. The authors also thank Meteorological and Hydrological Service of Croatia and the Ministry of Environmental and Nature Protection.

Funding Information

This research was funded by the Croatian Ministry of Science, Education and Sports.

Compliance with Ethical Standards

Conflict of interests

The authors declare they have no conflict of interest.

References

  1. Abdul-Wahab S, Al-Alawi S (2002) Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environ Modell Softw 17:219–228CrossRefGoogle Scholar
  2. Agency EP (2014) Air pollution: current and future challengesGoogle Scholar
  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 Modell Softw 23:396–403CrossRefGoogle Scholar
  4. Alkasassbeh M, Sheta AF, Faris H, Turabieh H (2013) Prediction of PM10 and TSP air pollution parameters using artificial neural network autoregressive, external input models: a case study in salt, Jordan. Middle-East J Sci Res 14:999–1009Google Scholar
  5. Banan N, Latif MT, Juneng L, Khan MF (2014) An application of artificial neural networks for the prediction of surface ozone concentrations in Malaysia. In: From Sources to Solution, Springer, pp 7–12Google Scholar
  6. Bandyopadhyay G, Chattopadhyay S (2007) Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone. Int J Environ Sci Technol 4:141–149CrossRefGoogle Scholar
  7. Belwal C, Sandu A, Constantinescu EM (2004) Adaptive resolution modeling of regional air quality. In: Proceedings of the 2004 ACM Symposium on Applied Computing, SAC ’04. ACM, New York, pp 235–239CrossRefGoogle Scholar
  8. Biancofiore F, Verdecchia M, Carlo PD, Tomassetti B, Aruffo E, Busilacchio M, Bianco S, Tommaso SD, Colangeli C (2015) Analysis of surface ozone using a recurrent neural network. Sci Total Environ 514:379–387CrossRefGoogle Scholar
  9. Chattopadhyay S (2007) Prediction of mean monthly total ozone time series–application of radial basis function network. Int J Remote Sens 28:4037–4046CrossRefGoogle Scholar
  10. Chen H, Tiňo P, Rodan A, Yao X (2014) Learning in the model space for cognitive fault diagnosis. IEEE Trans Neural Netw Learn Syst 25:124–136CrossRefGoogle Scholar
  11. Coman A, Ionescu A, Candau Y (2008) Hourly ozone prediction for a 24-h horizon using neural networks. Environ Modell Softw 23:1407–1421CrossRefGoogle Scholar
  12. Dan J, Guo W, Shi W, Fang B, Zhang T (2014) Deterministic echo state networks based stock price forecasting. In: Abstract and Applied Analysis, Hindawi Publishing Corporation, vol 2014Google Scholar
  13. Faris H, Alkasassbeh M, Rodan A (2014) Artificial neural networks for surface ozone prediction: models and analysis. Polish Journal of Environmental Studies 23(2)Google Scholar
  14. Feng Y, Zhang W, Sun D, Zhang L (2011) Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification. Atmos Environ 45:1979–1985CrossRefGoogle Scholar
  15. Fitzpatrick J, Cunningham R, Davidson J, Poots E (2010) Air pollution: action in a changing climate. Technical report http://www.defra.gov.uk
  16. Ghaly A (2012) Mapping environmental pollution, contamination, and waste in the United States. In: Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications. ACM, p 41Google Scholar
  17. Gómez P, Nebot A, Ribeiro S, Alquézar R, Mugica F, Wotawa F (2003) Local maximum ozone concentration prediction using soft computing methodologies. Syst Anal Modell Simul 43:1011–1031CrossRefGoogle Scholar
  18. Gorai A, Mitra G (2017) A comparative study of the feed forward back propagation (ffbp) and layer recurrent (lr) neural network model for forecasting ground level ozone concentration. Air Qual Atmos Health 10:213–223CrossRefGoogle Scholar
  19. Ha QP, Wahid H, Duc H, Azzi M (2015) Enhanced radial basis function neural networks for ozone level estimation. Neurocomputing 155:62–70CrossRefGoogle Scholar
  20. Hájek P, Olej V (2012) Ozone prediction on the basis of neural networks, support vector regression and methods with uncertainty. Ecol Inform 12:31–42CrossRefGoogle Scholar
  21. Hassoun M (1995) Fundamentals of Artificial Neural Networks. MIT Press, CambridgeGoogle Scholar
  22. Hornik KJ, Stinchcombe D, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366CrossRefGoogle Scholar
  23. Inal F (2010) Artificial neural network prediction of tropospheric ozone concentrations in Istanbul, Turkey. Clean–Soil Air Water 38:897–908CrossRefGoogle Scholar
  24. Jaeger H (2001) The “echo state” approach to analysing and training recurrent neural networks - with an erratum noteGoogle Scholar
  25. Kheirbek I, Wheeler K, Walters S, Pezeshki G, Kass D (2009) Air pollution and the health of new yorkers: the impact of fine particles and ozone. Technical report. New York City Department of Health and Mental Hygiene, East Lansing, MichiganGoogle Scholar
  26. Kisi O, Parmar KS, Soni K, Demir V (2017) Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and m5 model tree models. Air Qual Atmos Health 10:873–883CrossRefGoogle Scholar
  27. Kovač-Andrić E, Radanović T, Topalović I, Marković B, Sakač N (2013) Temporal variations in concentrations of ozone, nitrogen dioxide, and carbon monoxide at osijek, Croatia. Advances in MeteorologyGoogle Scholar
  28. Kovač-Andrić E, Sheta A, Faris H, Gajdošik MŠ (2016) Forecasting ozone concentrations in the east of Croatia using non-parametric neural network models. J Earth Syst Sci 125:997–1006CrossRefGoogle Scholar
  29. Kumar N, Middey A, Rao PS (2017) Prediction and examination of seasonal variation of ozone with meteorological parameter through artificial neural network at Neeri, Nagpur, India. Urban Clim 20:148–167CrossRefGoogle Scholar
  30. Liu Z, Liu A, Wang C, Niu Z (2004) Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification. Fut Gener Comput Syst 20:1119–1129CrossRefGoogle Scholar
  31. Luna A, Paredes M, de Oliveira G, Corrêa S (2014) Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil. Atmos Environ 98:98–104CrossRefGoogle Scholar
  32. Mak MW, Cho KW (1998) Genetic evolution of radial basis function centers for pattern classification. In: 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on Neural Networks Proceedings, vol 1. IEEE, pp 669–673Google Scholar
  33. Marco G, Bo X (2013) Air quality legislation and standards in the european union: background, status and public participation. Adv Clim Chang Res 4:50–59Google Scholar
  34. Nunnari G, Cannavó F (2007) A new cost function for air quality modeling. J VLSI Signal Process Syst 49:281–290CrossRefGoogle Scholar
  35. Peng H, Lima AR, Teakles A, Jin J, Cannon AJ, Hsieh WW (2017) Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods. Air Quality. Atmos Health 10:195–211CrossRefGoogle Scholar
  36. Radko K, Pavel Š (2015) The orediction of tropospheric ozone using a radial basis function network. Springer International Publishing, Cham, pp 115–123Google Scholar
  37. Rodan A, Tiňo P (2012) Simple deterministically constructed cycle reservoirs with regular jumps. Neural Computation, MIT Press, CambridgeGoogle Scholar
  38. Rodan A, Faris H (2016) Credit risk evaluation using cycle reservoir neural networks with support vector machines readout. Springer, Berlin, pp 595–604Google Scholar
  39. Salcedo-Sanz S, Camacho J, Pérez-Bellido ÁM, Ortíz-García EG, Portilla-Figueras A, Hernandez-Martin E (2011) Improving the prediction of average total ozone in column over the Iberian peninsula using neural networks banks. Neurocomputing 74:1492–1496CrossRefGoogle Scholar
  40. Selvaraj RS, Elampari K, Gayathri R, Jeyakumar SJ (2010) A neural network model for short term prediction of surface ozone at tropical city. Int J Eng Sci Technol 2:5306–5312Google Scholar
  41. Sexton RS, Dorsey RE, Johnson JD (1999) Optimization of neural networks: a comparative analysis of the genetic algorithm and simulated annealing. Eur J Oper Res 114:589–601CrossRefGoogle Scholar
  42. Sheta A, Ghatasheh N, Faris H (2015) Forecasting global carbon dioxide emission using auto-regressive with exogenous input and evolutionary product unit neural network models. In: The 6th International Conference on Information and Communication Systems (ICICS), pp 182–187Google Scholar
  43. Sheta AF, Faris H (2015) Influence of nitrogen-di-oxide, temperature and relative humidity on surface ozone modeling process using multigene symbolic regression genetic programming. Int J Adv Comput Sci Appl (IJACSA) 6:270–275Google Scholar
  44. Solaiman T, Coulibaly P, Kanaroglou P (2008) Ground-level ozone forecasting using data-driven methods. Air Qual Atmos Health 1:179–193CrossRefGoogle Scholar
  45. Sousa S, Martins F, Alvim-Ferraz M, Pereira MC (2007) Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environ Modell Softw 22:97–103CrossRefGoogle Scholar
  46. Tsai Ch, Chang Lc, Chiang Hc (2009) Forecasting of ozone episode days by cost-sensitive neural network methods. Sci Total Environ 407:2124–2135CrossRefGoogle Scholar
  47. Tsakiri KG, Zurbenko IG (2011) Prediction of ozone concentrations using atmospheric variables. Air Quality. Atmos Health 4:111–120CrossRefGoogle Scholar
  48. Vakil-Baghmisheh MT, Pavešić N (2004) Training RBF networks with selective backpropagation. Neurocomputing 62:39–64CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computing SciencesTexas A&M University-Corpus ChristiCorpus ChristiUSA
  2. 2.Business Information Technology Department, King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  3. 3.Higher Colleges of Technology, United Arab Emirates, and King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  4. 4.Department of ChemistryUniversity of J. J. StrossmayerOsijekCroatia

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