Share This Article:

Solving the Carbon Dioxide Emission Estimation Problem: An Artificial Neural Network Model

Abstract Full-Text HTML Download Download as PDF (Size:415KB) PP. 338-342
DOI: 10.4236/jsea.2013.67042    4,354 Downloads   6,136 Views   Citations

ABSTRACT

Climate Pollution due to the Carbon Emission (CO2) from the different fossil fuels is considered as a great and important international challenge to many researchers. In this paper we are providing a solution to forecast the poison CO2 gas emerged from energy consumption. Four inputs data were considered the global oil, natural gas, coal, and primary energy consumption to build our system. In this paper, we used the Artificial Neural Network (ANN) as successful and powerful tool in handling a time series modeling problem. The proposed ANN model was used to train and test the yearly CO2 Emission. The data were trained from year 1982 to 2000, and tested for the year 2003 to 2010. From the results obtained we can see that ANN performance was Excellent and proved its efficiency as a useful tool in solving the climate pollution problems.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

A. Baareh, "Solving the Carbon Dioxide Emission Estimation Problem: An Artificial Neural Network Model," Journal of Software Engineering and Applications, Vol. 6 No. 7, 2013, pp. 338-342. doi: 10.4236/jsea.2013.67042.

References

[1] M. R. Lotfalipour, M. A. Falahi and M. Ashena, “Economic Growth, CO2 Emissions, and Fossil Fuels Consumption in Iran,” Energy Policy, Vol. 2010, No. 35, 2010, pp. 5115-5120.
[2] H. Davoudpour and M. S. Ahadi, “The Potential for Greenhouse Gases Mitigation in Household Sector of Iran: Cases of Price Reform/Efficiency Improvement and Scenario for 2000-2010,” Energy Policy, Vol. 34, No. 1, 2006, pp. 40-49.
[3] I. A. Samoilov, A. I. Nakhutin, “Esimation and Meddium-Term Forecasting of Anthropogenic Carbon Dioxide and Methods Emission in Russia with Statistical Methods,” Vol. 34, No. 6, 2009, pp. 348-353.
[4] W. David, “Reduction in Carbon Dioxide Emissions: Estimating the Potential Contribution from Wind Power,” Renewable Energy Foundation, December 2004.
[5] M. A. Behrang,. E. Assareh, M. R. Assari and A. Ghanbarzadeh, “Using Bees Algorithm and Artificial Neural Network to Forecast World Carbon Dioxide Emission,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, Vol. 33, No. 19, 2011, pp. 1747-1759.
[6] H. T. Pao and C. M. Tsai, “Modeling and Forecasting the CO2 Emissions, Energy Consumption, and Economic Growth in Brazil,” Energy, Vol. 36, No. 5, 2011, pp. 2450-2458.
[7] N. Karunanithi, W. Grenney, D. Whitley and K. Bovee, “Neural Networks for River Flow Prediction,” Journal of Computing in Civil Engg, Vol. 8, No. 2, 1993, pp. 371-379.
[8] P. R. Bulando and J. Salas, “Forecasting of Short-Term Rainfall Using ARMA Models,” Journal of Hydrology, Vol. 144, No. 1-4, 1993, pp. 193-211.
[9] H. Hruschka, “Determining Market Response Functions by Neural Networks Modeling: A Comparison to Econometric Techniques,” European Journal of Operational Research, Vol. 66, 1993, pp. 867-888.
[10] E. Y. Li, “Artificial Neural Networks and Their Business Applications,” Information and Managements, Vol. 27, No. 5, 1994, pp. 303-313.
[11] K. Chakraborty, “Forecasting the Behavior of Multivariable Time Series Using Neural Networks,” Neural Networks, Vol. 5, 1992, pp. 962-970.
[12] G. Swales and Y. Yoon, “Applying Artificial Neural Networks to Investment Analysis,” Financial Analyst Journal, Vol. 48, No. 5, 1992, pp. 78-82.
[13] M. Negnevissky, “Artificial Intelligence: A Guide to Intelligent Systems,” 2nd Edition, Addison-Wesley, Boston, 2005.
[14] E. Y. Li, “Artificial Neural Networks and Their Business Applications,” Information and Managements, Vol. 27, No. 5, 1994, pp. 303-313.
[15] A. K. Baareh, A. Sheta and K. Al Khnaifes, “Forecasting River Flow in the USA: A Comparison between AutoRegression and Neural Network Non-Parametric Models,” Journal of Computer Science, Vol. 2, No. 10, 2006, pp. 775-780.
[16] M. S. Al-Batah, I. Mat, K. Z. Zamli and K. A. Azizli, “Modified Recursive Least Squares Algorithm to Train the Hybrid Multilayered Perceptron (HMLP) Network,” Applied Soft Computing, Vol. 10, No. 1, 2010, pp. 236-244.
[17] M. Seethe, I. V. Muralikrisha and B. L. Deekshatulu, “Artificial Neural Networks and Other Methods of Image Classifications,” Journal of Theoretical and Applied Information Technology, Vol. 4, No. 11, 2005, pp. 1039-1053.
[18] H. J. Lu, R. Setiono and H. Lui, “Effective Data Mining Using Neural Networks,” IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, 1996, pp. 957-961.
[19] O. Kisi, “River Flow Modeling Using Artificial Networks” Journal of Hydrologic Engineering, Vol. 9, No. 1, 2004, pp. 60-63.
[20] H. K. Cigizoglu and O. Kisi, “Flow Prediction by Three Back-Propagation Techniques Using K-Fold Partitioning of Neural Network Training Data,” Nordic Hydrology, Vol. 36, No. 1, 2005, pp. 49-64.
[21] A. K. Baareh, A. Sheta and K. Al Khnaifes, “Forecasting River Flow in the USA: A Comparison between AutoRegression and Neural Network Non-Parametric Models,” Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization, Lisbon, 22-24 September 2006, pp. 7-12.
[22] H. Kavoosi, M. H. Saidi, M. Kavoosi and M. Bohrng, “Forecast Global Carbon Dioxide Emission by Use of Genetic Algorithm (GA),” IJCSI International Journal of Computer Science Issues, Vol. 9, No. 5, 2012, pp. 418-427.

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.