# Estimation of Methane Gas Production in Turkey Using Machine Learning Methods
## 1 Introduction  
In order to ensure the continuity of living things in the world, there must be suitable climatic conditions and sufficient water resources. Although the world's climate has always been in a state of change, this change gained rapid momentum in today's world due to the activities of human beings, especially after the Industrial Revolution. Although its effects and consequences vary from region to region, the threat of climate change concerns all humanity. The first international step in this context is known as the United Nations Framework Convention on Climate Change, formed in 1992 [1; 2; 3; 4]. One of the most important factors affecting climate change is the increase in emissions of natural greenhouse gases such as methane (CH\({}_{4}\)), carbon dioxide (CO\({}_{2}\)), and diazo monoxide (N\({}_{2}\)O), which cannot be prevented due to continuing human activities. In order to reduce global warming by 1.5\({}^{\circ}\), an emission reduction of 45 per cent is required. From 2022 to 2023, this rate was found to be 1%. It was determined that the increase in methanegas emissions is very high, and 52 of 198 countries have not reported this under the United Nations Framework Convention on Climate Change [5]. When the literature was examined, many studies were found to focus on greenhouse gas and carbon dioxide emissions. However, methane gas, which plays a major role in global warming, is less well studied. In order to reach the target reduction of 1.5\({}^{\circ}\) in 2050, methane gas emissions, which are 27 times more effective than carbon dioxide in causing global warming, must be urgently taken under control [6,7]. The motivation of this study was to provide a guiding scientific approach in the formulation of policies necessary to control methane gas production by estimating its production. For this purpose, Turkey, which has both agricultural and industrial activities, and has not been able to reduce the use of coal in energy production to the desired level, was selected as a sample country for the study. The data set covering the years 1990-2020 was used in the study. When designing the model, the variables affecting the emission of methane gas, and the contributions of these variables to methane emission, were formed around three main categories: agricultural activities, fossil energy sources, and wastes. In the architecture of the model, the general variables of population and gross domestic product, which are considered highly influential to the above three categories, were added to the model. With these models, created using machine learning methods, Turkey's methane gas production was predicted with an average success rate of 93.6%. This study will create a vision for countries to reduce emissions by predicting methane gas. It is also expected to be a guide in the road map that Turkey will follow for emission reduction at COP 28, to be held in Dubai on 30 November 2023. The study is expected to contribute to the literature due to the lack of studies on methane gas. In the first part of the study, the importance and purpose of the research are emphasized. In the second part, similar studies in the literature are reviewed. Next, the characteristics of the data set and methodology are given and the design and results of the models are discussed. The results of the models are analyzed according to three different statistical methods and compared graphically. Finally, the contribution of the study's results to the literature are described. When the literature was examined, it was seen that most studies were situated within the framework of greenhouse gas and carbon dioxide emissions. By considering the effect of methane gas emissions on global warming, this study identified a gap in the literature. Table 1 shows the systematic literature review. \begin{table}
\begin{tabular}{c c c c c} \hline
**Author Details** & **Topic** & **Input/Output Variables** & **Machine Learning Technique** & **Conclusion** \\ \hline  & & Input: Literature review was conducted.