Estimation of Methane Gas Production in Turkey Using Machine Learning Methods
Source abstract
Abstract
Methane gas emission into the atmosphere is rising due to the use of fossil-based resources in post-industrial energy use, as well as the increase in food demand and organic wastes that comes with an increasing human population. For this reason, methane gas, which is among the greenhouse gases, is seen as an important cause of climate change along with carbon dioxide. The aim of this study was to predict, using machine learning, the emission of methane gas, which has a greater effect on the warming of the atmosphere than other greenhouse gases. Methane gas estimation in Turkey was carried out using machine learning methods. The R2 metric was calculated as logistic regression (LR) 94.9%, artificial neural networks (ANNs) 93.6%, and support vector regression (SVR) 92.3%. All three machine learning methods used in the study were close to ideal statistical criteria. LR had the least error and highest prediction success, followed by ANNs and then SVR. The models provided successful results, which will be useful in the formulation of policies in terms of animal production (especially cattle production) and the disposal of organic human wastes, which are thought to be the main causes of methane gas emission.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Air Quality Monitoring and Forecasting · Atmospheric and Environmental Gas Dynamics · Energy Load and Power Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Business Administration, Faculty of Economics and Administrative Sciences, Akdeniz University, Antalya 07058, Turkey
Accounting and Tax Department, Korkuteli Vocational School, Akdeniz University, Antalya 07800, Turkey
Department of Management Information Systems, Faculty of Manavgat Social Sciences and Humanities, Akdeniz University, Antalya 07600, Turkey
Department of Mechanical Engineering, Faculty of Engineering, Akdeniz University, Antalya 07058, Turkey
Department of Management Information Systems, Faculty of Applied Sciences, Akdeniz University, Antalya 07058, Turkey
Management and Organization Department, Aydin Vocational School, Adnan Menderes University, Aydın 09010, Turkey
References (43)
Show source-provided references
1. The United Nations framework convention on climate change10.1111/j.1467-9388.1992.tb00046.x ↗
2. The United Nations framework convention on climate change: A commentary
3. Climate dynamics and global change10.1146/annurev.fl.26.010194.002033 ↗
4. Climate change and the ecologist10.1038/448550a ↗
5. (2023, March 10). C. (COP27). Assessment Reports. Available online: https://cop27.eg/#/.
6. Prospects of Low and Zero-Carbon Renewable fuels in 1.5-Degree Net Zero Emission Actualisation by 2050: A Critical Review10.1016/j.ccst.2022.100072 ↗
7. There is no Plan B for dealing with the climate crisis10.1080/00963402.2019.1654255 ↗
8. Greenhouse gas emissions from soils—A review10.1016/j.chemer.2016.04.002 ↗
9. Garip, E., and Oktay, A.B. (2018, January 28–30). Forecasting CO2 Emission with Machine Learning Methods. Proceedings of the 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey.
10. Solving the carbon dioxide emission estimation problem: An artificial neural network model10.4236/jsea.2013.67042 ↗
11. Machine learning based analysis for relation between global temperature and concentrations of greenhouse gases
12. Machine learning for predicting greenhouse gas emissions from agricultural soils10.1016/j.scitotenv.2020.140338 ↗
13. Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems10.1088/1748-9326/abd2f3 ↗
14. Machine-learning algorithms for predicting land susceptibility to dust emissions: The case of the Jazmurian Basin, Iran10.1016/j.apr.2020.05.009 ↗
15. Forecasting of Greenhouse Gas Emissions in Turkey using Machine Learning Methods
16. CO2 Emissions in Turkey and EU Countries Using Machine Learning Algorithms
17. Modelling carbon dioxide emissions under a maize-soy rotation using machine learning10.1016/j.biosystemseng.2021.09.013 ↗
18. Kerimov, B., and Chernyshev, R. (2022, January 21–22). Review of machine learning methods in the estimation of greenhouse gas emissions. Proceedings of the International Conference of Young Scientists Modern Problems of Earth Sciences, Tbilisi, Georgia.
19. Carbon dioxide emission prediction using support vector machine10.1088/1757-899X/114/1/012148 ↗
20. Ensemble machine learning for modeling greenhouse gas emissions at different time scales from irrigated paddy fields10.1016/j.fcr.2023.108821 ↗
21. Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review
22. El Naqa, I., and Murphy, M.J. (2015). What is Machine Learning?, Springer.10.1007/978-3-319-18305-3_1 ↗
23. Haldorai, A., Ramu, A., and Suriya, M. (2020). Business Intelligence for Enterprise Internet of Things, Springer.10.1007/978-3-030-44407-5 ↗
24. Understanding logistic regression analysis10.11613/BM.2014.003 ↗
25. Multinomial Logistic Regression: Usage and Application in Risk Analysis
26. Odds Ratios and Logistic Regression: Further Examples of their use and Interpretation10.1177/1536867X0300300301 ↗
27. Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review10.1016/j.aci.2018.10.001 ↗
28. Introduction to artificial neural network
29. Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs10.1016/j.matcom.2020.07.011 ↗
30. Vapnik, V. (1999). The Nature of Statistical Learning Theory, Springer Science & Business Media.10.1007/978-1-4757-3264-1 ↗
31. An overview of statistical learning theory10.1109/72.788640 ↗
32. Predicting stock price trends based on financial news articles and using a novel twin support vector machine with fuzzy hyperplane10.1016/j.asoc.2020.106806 ↗
33. Cross Validation Through Two-Dimensional Solution Surface for Cost-Sensitive SVM10.1109/TPAMI.2016.2578326 ↗
34. Normalization in the selection of construction alternatives
35. A study on normalization techniques for privacy preserving data mining
36. Cross-validation: A review
37. Cross-validation of regression models10.1080/01621459.1984.10478083 ↗
38. Correlation and the coefficient of determination10.1037/0033-2909.97.2.307 ↗
39. Di Bucchianico, A. (2008). Encyclopedia of Statistics in Quality and Reliability, Wiley Online Library.
40. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance10.3354/cr030079 ↗
41. Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature10.5194/gmd-7-1247-2014 ↗
42. Mean squared error of prediction as a criterion for evaluating and comparing system models10.1016/0304-3800(89)90035-5 ↗
43. Minimum mean squared error equalization using a priori information10.1109/78.984761 ↗