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.
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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.
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Pauzi, H.M., Abdullah, L. (2014). Performance Comparison of Two Fuzzy Based Models in Predicting Carbon Dioxide Emissions. In: Herawan, T., Deris, M., Abawajy, J. (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Lecture Notes in Electrical Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-4585-18-7_24
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DOI: https://doi.org/10.1007/978-981-4585-18-7_24
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