# Estimation of Methane Gas Production in Turkey Using Machine Learning Methods
## 4 Conclusions  
The aim of this study was to use machine learning to predict the emission of methane gas, which has a greater effect on the warming of the atmosphere than other greenhouse gases. The differentiating aspect of this study from other studies in the literature is that greenhouse gas emission studies have so far either predicted greenhouse gas emissions in general or focused on carbon dioxide emissions. However, the atmospheric heating potential of methane gas is higher than other greenhouse gases. This study was also unique in its use of not only technical data but also economic and environmental factors, which were accepted as variables in the design of the model.  
In the study, three different machine learning techniques were applied to find the best machine learning technique. The aim was to provide preliminary information for researchers that the machine learning technique works successfully, so they can use this model in the future. In terms of the usability of the model and the accessibility of the data set, it was thought to be a guiding study, especially in making decisions to prevent methane gas and in the creation of sustainable policies. In particular, the model provided statistically very useful and successful results that can be used in the formulation of policies in terms of animal production (especially cattle production) and the disposal of organic human waste, which is considered to be the main cause of methane gas emission.  
The results revealed that the methane gas emission variables used in the study can be considered as variables for future greenhouse gas emission estimation studies.  
In this study, methane gas production in Turkey was successfully estimated using machine learning methods. Three different supervised machine learning methods--logistic regression, artificial neural networks, and support vector regression--were used in the study. To analyze and evaluate these methods, R\({}^{2}\), MAE, and MSE metrics were calculated. R\({}^{2}\) was found to be 94.9% for logistic regression, 93.6% for artificial neural networks, and 92.3% for support vector regression. These results show that the ideal values were met. MSE was 0.052 for logistic regression, 0.053 for artificial neural networks, and 0.065 for support vector regression. MAE was considered successful in all three models. MSE was  
Figure 8: Line plot of machine learning models.  
0.005 for logistic regression, 0.006 for artificial neural networks, and 0.009 for support vector regression. The error rate of the models was found to be low and acceptable. According to the results of the analyses, it can be seen that the success of all three models was high, and the error was within the acceptable range. The model was also more successful than that of similar studies. According to the statistical metrics, logistic regression, artificial neural networks, and support vector regression were all successful models, with the least error obtained with logistic regression, for methane gas production forecasting in Turkey.  
Methodology, M.K., B.T., G.T. and G.F.U.U.; formal analysis, M.K. and A.C.; data curation, M.T., B.T. and G.T.; writing--original draft, M.K., A.C., B.T., M.T., R.K.Y. and G.F.U.U.; writing--review and editing, B.T., G.T., M.T., R.K.Y., G.F.U.U., A.C. and R.K.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.