Performance Comparison of Two Fuzzy Based Models in Predicting Carbon Dioxide Emissions

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

Many studies have been carried out worldwide to predict carbon dioxide (CO2) emissions using various methods. Most of the methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity of the data. Fuzzy inference system (FIS) and adaptive neuro fuzzy inference system (ANFIS) are two of the well-known methods with its ability to handle the problems of non-linearity. However, the performances of these two fuzzy based models in predicting CO2 emissions are not immediately known. This paper offers the performance comparison of the two fuzzy based models in prediction of CO2 emissions in Malaysia. The inputs for the models were simulated using the Malaysian data for the period of 1980–2009. The prediction performances were measured using root means square error, mean absolute error and mean absolute percentage error. The performances of the two models against the CO2 emission clearly show that the ANFIS outperforms the FIS model.

Keywords

Artificial intelligence CO2 emissions Fuzzy inference systems Prediction 

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Notes

Acknowledgments

The authors are grateful to the Malaysian Ministry of Higher Education and University Malaysia Terengganu for financial support under the FRGS grant number 59243.

References

  1. 1.
    Intergovernmental Panel Climate Change (IPCC). Synthesis Report, Geneva, Switzerland (2007).Google Scholar
  2. 2.
    Pokrovsky, O.M., Kwok, Roger H.F., Ng, C.N. : Fuzzy logic approach for description of meteorological impacts on urban air pollution species.. A Hong Kong case study. Computers & Geosciences, 28, 119–127 (2002).Google Scholar
  3. 3.
    Marino, D., Morabito, F. C., Ricca, B.:Management of uncertainty in environmental problems: an assessment of technical aspects and policies. Handbook of Uncertainty, J. Gil Aluja, Ed., Kluwer Academic Publisher (2001).Google Scholar
  4. 4.
    Pao, H.T., Fu, H.C., Tseng, C.L.: Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model.Energy.40(1),400–409 (2012).Google Scholar
  5. 5.
    Pao, H.T., Tsai, C.M. : Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil. Energy. 36(5), 2450–2458 (2011).Google Scholar
  6. 6.
    Lin, C.S., Liou, F.M.,Huang, C.P.: Grey forecasting model for CO2 emissions: A Taiwan study. Applied Energy. 88, 3816–3820 (2011).Google Scholar
  7. 7.
    Lu, I.J., Lewis, C., Lin, S.J.: The forecast of motor vehicle, energy demand and CO2 emission from Taiwan’s road transportation sector, Energy Policy 37(8), 2952–2961 (2009).Google Scholar
  8. 8.
    Radojević, D., Pocajt, V., Popović, I., Perić-Grujić, A., Ristić, M. Forecasting of Greenhouse Gas Emission in Serbia Using Artificial Neural Networks, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 35(8), 733–740 (2013).Google Scholar
  9. 9.
    Liu, P., Zhang, G., Zhang, X., Cheng, S. : Carbon Emissions Modeling of China Using Neural Network. Computational Sciences and Optimization (CSO), Fifth International Joint Conference, pp.679–682 (2012).Google Scholar
  10. 10.
    Yap, W.K, Karri, V. Emissions predictive modelling by investigating various neural network models.Expert Syst. Appl. 39(3), 2421–2426 (2012).Google Scholar
  11. 11.
    Li, S., Zhou, R., Ma, X. : The forecast of CO2 emissions in China based on RBF neural networks. Industrial and Information Systems (IIS), 2nd International Conference, pp.319–322 (2010).Google Scholar
  12. 12.
    Sözen, A., Gülseven, Z., Arcaklioğlu, E. : Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies. Energy Policy. 35(12), 6491–6505 (2007).Google Scholar
  13. 13.
    Feng, Y.Y., Chen, S.Q., Zhang, L.X. :System dynamics modeling for urban energy consumption and CO2 emissions: A case study of Beijing, China, Ecol. Model. 252,44–52 (2013).Google Scholar
  14. 14.
    Zhao, J., Zhang, J., Jia, S., Li, Q., Zhu, Y. A: MapReduce framework for on-road mobile fossil fuel combustion CO2 emission estimation.Geoinformatics, 19th International Conference, pp.1–4 (2011).Google Scholar
  15. 15.
    Mintz, R., Young, B. R.,Svrcek, W. Y.: Fuzzy logic modeling of surface ozone concentrations.Computers and Chemical Engineering.29, 2049–2059 (2005).Google Scholar
  16. 16.
    Peche, R., Rodríguez, E.: Environmental impact assessment procedure: A new approach based on fuzzy logic. Environmental Impact Assessment Review.29, 275–283 (2009).Google Scholar
  17. 17.
    Huang, Y., Chen, X., Li, Y. P., Huang, G.H., Liu, T. : A fuzzy-based simulation method for modelling hydrological processes under uncertainty.Hydrol.Process.24, 3718–3732 (2010).Google Scholar
  18. 18.
    Carbajal-Hernández, J. J., Sánchez-Fernández, L.P., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F.: Assessment and prediction of air quality using fuzzy logic and autoregressive Models. Atmospheric Environment, 60, 37–50. (2012).Google Scholar
  19. 19.
    Yetilmezsoy, K., & Abdul-Wahab, S.A. : A prognostic approach based on fuzzy-logic methodology to forecast PM10 levels in Khaldiya residential area, Kuwait. Aerosol and Air Quality Research,12,1217–1236 (2012).Google Scholar
  20. 20.
    Antanasijević, D.Z., Pocajt, V.V., Povrenović, D.S., Ristić, M.Đ., Perić-Grujić, A.A., : PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci. Total Environ 443,511–519 (2012).Google Scholar
  21. 21.
    Yu, Z., Liangsheng, L.,& Changhai, S.: Evaluation of the Levels of Manufacturers Developing Low-Carbon on BP Neural Network.E-Product E-Service and E-Entertainment (ICEEE), 2010 International Conference,pp.1-4 (2010).Google Scholar
  22. 22.
    Lim, Y., Moon, Y.S.,& Kim, T.W. :Artificial neural network approach for prediction of ammonia emission from field-applied manure and relative significance assessment of ammonia emission factors.Europ. J. Agronomy. 26, 425–434 (2007).Google Scholar
  23. 23.
    Jain, S., & Khare.M.: Adaptive neuro-fuzzy modeling for prediction of ambient CO concentration at urban intersections and roadways. Air Quality, Atmosphere & Health, 3, 203–212 (2010).Google Scholar
  24. 24.
    Morabito, F.C., & Versaci, M.: Fuzzy Neural Identification and forecasting techniques to process experimental urban air pollution data., Neural Network.16, 493–506 (2003).Google Scholar
  25. 25.
    Noori, R., Hoshyaripour, G., Ashrafi, K., & Araabi, B.N.: Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment 44(4), 476–482 (2010).Google Scholar
  26. 26.
    Campbell, P. R J. : Comparison of fuzzy modelling techniques for load forecasting,. Fuzzy Systems Conference, 2007.FUZZ-IEEE 2007. IEEE International, pp.1,5. (2007).Google Scholar
  27. 27.
    Badri, A., Ameli, Z., & Birjandi, A. M.: Application of artificial neural networks and fuzzy logic methods for short term load forecasting, Energy Procedia, 1883–1888 (2012).Google Scholar
  28. 28.
    Lohani, A.K., Kumar, R., & Singh, R.D. :Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. Journal of Hydrology, 442–443, 23–35 (2012).Google Scholar
  29. 29.
    Chen, M.S., Ying, L.C., & Pan, M.C.: Forecasting tourist arrivals by using the adaptive network-based fuzzy inference system.Expert Systems with Applica-tions. 37, 1185–1191 (2010).Google Scholar
  30. 30.
    World Bank 2011. World Development Indicators [Online].http://data.worldbank.org/, (2011).
  31. 31.
    Palani, S.,Liong, S.Y., & Tkalich, P.: An ANN application for water quality forecasting. Mar. Pollut. Bull., 56, 1586–1597 (2008).Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Department of Mathematics, Faculty of Science and TechnologyUniversity Malaysia TerengganuKuala TerengganuMalaysia

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