The week's pick
Random Articles
Reseach Article
Forecast Global Carbon Dioxide Emission using Swarm Intelligence
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 77 - Number 12 |
| Year of Publication: 2013 |
| Authors: Alaa Abdelfatah, Sahar A. Mokhtar, Alaa Sheta, Basma Solaiman |
10.5120/13443-1343
|
Alaa Abdelfatah, Sahar A. Mokhtar, Alaa Sheta, Basma Solaiman . Forecast Global Carbon Dioxide Emission using Swarm Intelligence. International Journal of Computer Applications. 77, 12 ( September 2013), 1-5. DOI=10.5120/13443-1343
Abstract
The tremendous effects of air quality in large cities have been considered a severe environmental problem all over the world. Therefore, the international community agreed to develop air quality standards to monitor and control pollution rates around industrial communities. Harmful emission into the air is a sign that could extremely affect man's health, natural life and agriculture. Forecasting models is essential for predicting air quality. CO2 emissions have been an international concern because of fossil fuels. In this study, Particle Swarm Optimization (PSO) is used for analyzing world CO2 emission based on the global energy consumption. A parametric PSO model is developed to forecast CO2 emission based set of attributes. They include: global oil, natural gas, coal, and primary energy consumption. A data set collected during the years 1980 and 2010 were used in this study. Experimental results show that PSO can provide good modeling results using a limited number of measurements compared to other linear models.
References
- Mouhammd Al-kasassbeh, Alaa F. Sheta, Hossam Faris, and Hamza Turabieh. Prediction of pm10 and tsp air pollution parameters using artificial neural network autoregressive, external input models: A case study in salt, jordan. Middle- East Journal of Scientific Research, 14(7):999–1009, 2013.
- Maximilian Auffhammer and Richard T. Carson. Forecasting the path of china's CO2 emissions using province-level information. Journal of Environmental Economics and Management, 55(3):229–247, May 2008.
- Maximilian Auffhammer and Ralf Steinhauser. Forecasting the path of U. S. CO2 emissions using state-level information. The Review of Economics and Statistics, 94(1):172–185, February 2012.
- A. Baareh. Solving the carbon dioxide emission estimation problem: An artificial neural network model. Journal of Software Engineering and Applications, 6(7):338–342, 2013.
- 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, 33(19):1747–1759, 2011.
- B. Birge. PSOt - a particle swarm optimization toolbox for use with Matlab. In Proceedings of the 2003 IEEE Swarm Intelligence Symposium (SIS03), pages 182–186, 2003.
- D. Bratton and J. Kennedy. Defining a standard for particle swarm optimization. In IEEE Swarm Intelligence Symposium, pages 120 – 127, 2007.
- E. Nnaji Chibueze, O. Chukwu Jude, and Nnaji Moses. Electricity supply, fossil fuel consumption, CO2 emissions and economic growth: Implications and policy options for sustainable development in nigeria. International Journal of Energy Economics and Policy, 3(3):262–271, 2013.
- Andries P. Engelbrecht. Computational Intelligence: An Introduction. Wiley Publishing, 2nd edition edition, 2007.
- Kostas D. Karatzas, George Papadourakis, and Ioannis Kyriakidis. Understanding and forecasting atmospheric quality parameters with the aid of ANNs. In IEEE International Joint Conference on Neural Networks (IJCNN 2008), pages 2580–2587. IEEE, 2008.
- H Kavoosi, MH Saidi, M Kavoosi, and M Bohrng. Forecast global carbon dioxide emission by use of genetic algorithm. International Journal of Computer Science Issues (IJCSI), 9(1), 2012.
- Mohammad Reza Lotfalipour, Mohammad Ali Falahi, and NULL NULL. Economic growth, CO2 emissions, and fossil fuels consumption in Iran. Energy, (35):5115–5120, October 2010.
- Marek Makowski. Modeling paradigms applied to the analysis of european air quality. European Journal of Operational Research, 122(2):219–241, 2000.
- Bert Metz. Climate Change 2007-Mitigation of Climate Change: Working Group III Contribution to the Fourth Assessment Report of the IPCC, volume 4. Cambridge University Press, 2007.
- Francesco Carlo Morabito and Mario Versaci. Fuzzy neural identification and forecasting techniques to processexperimental urban air pollution data. Neural Networks, 16(3- 4):493–506, 2003.
- A. Newcomer, S. Blumsack, J. Apt, L. B. Lave, and M. G. Morgan, editors. Electricity Load and Carbon Dioxide Emissions: Effects of a Carbon Price in the Short Term. 41st Hawaii International Conference on System Sciences, Computer Society Press, 2008.
- Hsiao-Tien Pao and Chung-Ming Tsai. Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in brazil. Energy, 36(5):2450 – 2458, 2011.
- Reza Samsami. Application of ant colony optimization (ACO) to forecast CO2 emission in Iran. International Journal of Environment, Pharmacology and Life Sciences, Bulletin of Environment, Pharmacology and Life Sciences, 2:95–99, 2013.
- Shou Hsing Shih and Chris P. Tsokos. Prediction models for carbon dioxide emissions and the atmosphere. Neural, Parallel Sci. Comput. , 16(1):165–178, January 2008.
- Ioan Cristian Trelea. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85(6):317–325, 2003.
- Takashi Washio, Einoshin Suzuki, Kai Ming Ting, and Akihiro Inokuchi, editors. Advances in Knowledge Discovery and Data Mining, Proceedings of the 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, May 20-23, volume 5012 of Lecture Notes in Computer Science. Springer, 2008.
Index Terms
Keywords