Quality & Quantity

, Volume 49, Issue 6, pp 2633–2647 | Cite as

Artificial neural networks and fuzzy time series forecasting: an application to air quality

  • Nur Haizum Abd Rahman
  • Muhammad Hisyam LeeEmail author
  • Suhartono
  • Mohd Talib Latif


The arising air pollution has addressed much attention globally due to its detrimental effects on human health and environment. As an early warning system for air quality control and management, it is important to provide precise information about the future concentrations in pollutants. We present here a time series model in predicting the Air Pollution Index (API) from three different stations; industrial, residential, and sub-urban areas between 2000 and 2009. In this paper, the Box–Jenkins approach of seasonal autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and three models of fuzzy time series (FTS) have been compared by using the mean absolute percentage error, mean absolute error, mean square error, and root mean square error. Although all the methods were used as operational tools, the ANN seemed more accurate in forecasting API. The results showed that FTS (i.e. Chen’s, Yu’s, and Cheng’s) performed inconsistent results since the conventional methods of ARIMA outperformed the performance of FTS. However, consistent results were achieved as the ANNs gave the smallest forecasting error compared to FTS and ARIMA.


Artificial neural network Air Pollution Index (API) Time series Forecasting Fuzzy time series ARIMA 


  1. Afroz, R., Hassan, M.N., Ibrahim, N.A.: Review of air pollution and health impacts in Malaysia. Environ. Res. 92(2), 71–77 (2003)CrossRefGoogle Scholar
  2. Armstrong, J.S., Collopy, F.: Error measures for generalizing about forecasting methods: empirical comparisons. Int. J. Forecast. 8(1), 69–80 (1992). doi: 10.1016/0169-2070(92)90008-w CrossRefGoogle Scholar
  3. Bernard, E.A.: F.: Fuzzy approaches to environmental decisions: application to air quality. Environ. Sci. Policy 9(1), 22–31 (2006). doi: 10.1016/j.envsci.2005.08.006 CrossRefGoogle Scholar
  4. Bernard, F.: Fuzzy environmental decision-making: applications to air pollution. Atmos. Environ. 37(14), 1865–1877 (2003). doi: 10.1016/s1352-2310(03)00028-1 CrossRefGoogle Scholar
  5. Caselli, M., Trizio, L., Gennaro, Gd, Ielpo, P.: A simple feedforward neural network for the PM10 forecasting: comparison with a radial basis function network and a multivariate linear regression model. Water Air Soil Pollut. 201, 365–377 (2009). doi: 10.1007/s11270-008-9950-2 CrossRefGoogle Scholar
  6. Chen, S.M.: Forecasting enrollments based on high-order fuzzy time series. Cybern. Syst. 33(1), 1–16 (2002)CrossRefGoogle Scholar
  7. Cheng, C.-H., Chen, T.-L., Teoh, H.J., Chiang, C.-H.: Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert Syst. Appl. 34(2), 1126–1132 (2008)CrossRefGoogle Scholar
  8. Cryer, J.D.: Time Series Analysis, 1st edn. Duxbury Press, Belmont (1986)Google Scholar
  9. Faraway, J., Chatfield, C.: Time series forecasting with neural networks: a comparative study using the air line data. J. R. Stat. Soc.: Series C 47(2), 231–250 (1998). doi: 10.1111/1467-9876.00109 CrossRefGoogle Scholar
  10. Hanke, J.E., Wichern, D.W.: Business Forecasting, 8th edn. Pearson/Prentice Hall, Upper Saddle River (2005)Google Scholar
  11. Hassanzadeh, S., Hosseinibalam, F., Alizadeh, R.: Statistical models and time series forecasting of sulfur dioxide: a case study Tehran. Environ. Monit. Assess. 155(1), 149–155 (2009). doi: 10.1007/s10661-008-0424-1 CrossRefGoogle Scholar
  12. Heo, J.-S., Kim, D.-S.: A new method of ozone forecasting using fuzzy expert and neural network systems. Sci. Total Environ. 325(1–3), 221–237 (2004). doi: 10.1016/j.scitotenv.2003.11.009 CrossRefGoogle Scholar
  13. Hui-Kuang, Y.: Weighted fuzzy time series models for TAIEX forecasting. Phys. A: Stat. Mech. Appl. 349(3–4), 609–624 (2005)Google Scholar
  14. Ibrahim, M.Z., Zailan, R., Ismail, M., Lola, M.S.: Forecasting and time series analysis of air pollutants in several area of Malaysia. Am. J. Environ. Sci. 5(5), 625–632 (2009). doi: 10.3844/ajessp.2009.625.632 CrossRefGoogle Scholar
  15. Kampa, M., Castanas, E.: Human health effects of air pollution. Environ. Pollut. 151(2), 362–367 (2008)CrossRefGoogle Scholar
  16. Kumar, A., Goyal, P.: Forecasting of daily air quality index in Delhi. Sci. Total Environ. 409(24), 5517–5523 (2011). doi: 10.1016/j.scitotenv.2011.08.069 CrossRefGoogle Scholar
  17. Kurt, A., Oktay, A.B.: Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks. Expert Syst. Appl. 37(12), 7986–7992 (2010)CrossRefGoogle Scholar
  18. Morabito, F.C., Versaci, M.: Fuzzy neural identification and forecasting techniques to process experimental urban air pollution data. Neural Netw. 16(3–4), 493–506 (2003). doi: 10.1016/s0893-6080(03)00019-4 CrossRefGoogle Scholar
  19. Perez, P., Salini, G.: PM2.5 Forecasting in a large city: comparison of three methods. Atmos. Environ. 42(35), 8219–8224 (2008)CrossRefGoogle Scholar
  20. Rizzo, A., Glasson, J.: Iskandar Malaysia. Cities(0) (2011). doi: 10.1016/j.cities.2011.03.003
  21. Sansuddin, N., Ramli, N., Yahaya, A., Yusof, N., Ghazali, N., Madhoun, W.: Statistical analysis of PM10 concentrations at different locations in Malaysia. Environ. Monit. Assess. 180(1), 573–588 (2011). doi: 10.1007/s10661-010-1806-8 CrossRefGoogle Scholar
  22. Shyi-Ming, C.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81(3), 311–319 (1996)CrossRefGoogle Scholar
  23. Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series – Part I. Fuzzy Sets Syst. 54(1), 1–9 (1993a)CrossRefGoogle Scholar
  24. Song, Q., Chissom, B.S.: Fuzzy time series and its models. Fuzzy Sets Syst. 54(3), 269–277 (1993b)CrossRefGoogle Scholar
  25. Wang, X.K., Lu, W.Z.: Seasonal variation of air pollution index: Hong Kong case study. Chemosphere 63(8), 1261–1272 (2006)CrossRefGoogle Scholar
  26. World Resources Institute: (2002) Rising Energy Use: Health Effects of Air Pollution. World Resources Institute. (2002). Accessed January 10, 2011
  27. Zhang, G., Eddy Patuwo, B., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998). doi: 10.1016/s0169-2070(97)00044-7 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Nur Haizum Abd Rahman
    • 1
  • Muhammad Hisyam Lee
    • 1
    Email author
  • Suhartono
    • 2
  • Mohd Talib Latif
    • 3
  1. 1.Department of Mathematical Sciences, Faculty of ScienceUniversiti Teknologi MalaysiaSkudaiMalaysia
  2. 2.Department of Statistics, Faculty of Mathematics and Natural SciencesInstitut Teknologi Sepuluh NopemberSurabayaIndonesia
  3. 3.School of Environmental and Natural Resource Sciences, Faculty of Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia

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