Title: Nonlinear modelling for greenhouse effect related to global warming incorporated with the nuclear industry using neural networking theory
Authors: Tae Ho Woo; Hyo Sung Cho
Addresses: Department of Mechanical and Control Engineering, The Cyber University of Korea, 106 Bukchon-ro, Jongno-gu, Seoul 03051, South Korea ' Department of Radiation Convergence Engineering, Yonsei University, Wonju 26493, South Korea
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.
Keywords: greenhouse; global warming; nuclear; power; neural networking.
International Journal of Global Warming, 2018 Vol.16 No.3, pp.337 - 346
Received: 13 Jan 2018
Accepted: 27 Mar 2018
Published online: 21 Sep 2018 *