Nonlinear modelling for greenhouse effect related to global warming incorporated with the nuclear industry using neural networking theory Online publication date: Wed, 03-Oct-2018
by Tae Ho Woo; Hyo Sung Cho
International Journal of Global Warming (IJGW), Vol. 16, No. 3, 2018
Abstract: The greenhouse effect related global warming is studied by the artificial intelligence (AI) based neural networking modelling. The stochastic impacts by regression on population, affluence and technology (STIRPAT) model is applied to the carbon dioxide gas based greenhouse effect. It is shown of the energy factor where four sources as oil, coal, nuclear, and renewable are connected in which the values are cumulative for comparisons with nuclear energy. The values are higher in the case of 'without nuclear'. The highest values are 1,808.78 and 6,609.68 respectively which are dimensionless and means for the global warming effectiveness. It is also described as the global warming factor for comparisons with nuclear power in which the values show higher in the later time. The study shows the dynamical transients of the negative effects in the case of the nuclear energy portion missing. Therefore, the effect of the nuclear part should be considered as one of major energy sources of a nation.
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