Publication: Forecasting greenhouse gas emissions based on different machine learning algorithms
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Date
2022-01-01
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Springer, Cham
Abstract
With the increase in greenhouse gas emissions, climate change is
occurring in the atmosphere. Although the energy production for Turkey is
increased at a high rate, the greenhouse gas emissions are still high currently.
Problems that seem to be very complex can be predicted with different algorithms without difficulty. Due to fact that artificial intelligence is often included
in the studies to evaluate the solution performance and make comparisons with
the obtained solutions. In this study, machine learning algorithms are used to
compare and predict greenhouse gas emissions. Carbon dioxide (CO2), nitrous
oxide (N2O), methane (CH4), and fluorinated gases (F-gases) are considered
direct greenhouse gases originating from the agriculture and waste sectors,
energy, industrial processes, and product use, within the scope of greenhouse
gas emission statistics. Compared to different machine learning methods, support vector machines can be considered an advantageous estimation method
since they can generalize more details. On the other hand, the artificial neural
network algorithm is one of the most commonly used machine learning algorithms in terms of classification, optimization, estimation, regression, and pattern tracking. From this point of view, this study aims to predict greenhouse gas
emissions using artificial neural network algorithms and support vector machines by estimating CO2, CH4, N2O, and F-gases from greenhouse gases. The data
set was obtained from the Turkish Statistical Institute and the years are included
between 1990 and 2019. All analyzes were performed using MATLAB version
2019b software.
Description
Keywords
Machine learning algorithm, Greenhouse gases, Forecasting
Citation
ÜLKÜ İ., ÜLKÜ E. E., Forecasting Greenhouse Gas Emissions Based on Different Machine Learning Algorithms, "Lecture Notes in Networks and Systems", , Editör, Springer, Cham, ss.109-116, 2022