# Estimation of Methane Gas Production in Turkey Using Machine Learning Methods
## 2 Materials and Methods
### Data Set  
Turkey, which has both agricultural and industrial activities and cannot reduce coal use in energy production to the desired level, was selected as a sample country. Data covering the annual period between 1990 and 2020 were used. The data set was created from the data obtained from the Turkish Statistical Institute (TUIK) database. The data set consisted of 372 data in total. While designing the model in the study, the variables affecting the emission of methane gas and the contribution of these variables to methane emission were formed around three main categories. The first and most important factor influencing methane gas emission is the agricultural activity of a country. The largest methane gas emission resulting from agricultural activities is caused by enteric fermentation. Enteric fermentation is the digestion of food by microorganisms in the digestive system of cattle. As a result of this process, methane gas is formed as a by-product. For this reason, the number of cattle in Turkey was determined as the input variable for red meat production in the model. Another agricultural activity that causes methane gas emissions is paddy cultivation. During the cultivation process of paddy, a large amount of water must be kept on the field surface for a long time. As a result, the organic materials in the soil remain in an oxygen-free environment and cause methane gas emission. Considering this situation in paddy cultivation, the surface area of cultivated paddy fields was added to the model. The last methane gas emission factor, which was accepted as an input variable in the category of agricultural activities, is the burning of biomass in agricultural areas. As a result of this combustion, methane gas is released to the atmosphere from the organic residues in these areas. The size of agricultural surface area and agricultural greenhouse gas emissions were, therefore, included in the model. The second main category of causative factors for methane gas emissions is the production and utilization processes of coal and natural gas, which are fossil energy sources. During the production (including enrichment), storage, and distribution phases of natural gas, methane gas, which is the main component of this gas, is released and causes emissions. At the same time, methane emissions occur during the extraction of coal, another fossil-based fuel (especially in closed underground mines). The share of these two fossil fuels in total energy production was added to the model as a variable. Since these two fossil fuels are used especially in manufacturing and heavy industry, the industrial production index was also added to the model. The third main factor category in the formation of methane gas emission is waste. Especially in landfill areas, where domestic wastes accumulate, there is a high rate of organic waste, which increases methane gas emission. For this reason, greenhouse gas emissions of wastes were also added to the model. The general variables of population and gross domestic product of the country, which are considered to affect all three main categories of emission causes, were also added to the model. The model is shown in Table 2.  
In designing machine learning models, the properties of the independent variables are important. The average, maximum, minimum, and standard deviation (SD) of the data used in the model are given in Table 3.