TY  - JOUR
AU  - Arya, Suraj
AU  - Anju
AU  - Onah, Jonas Nnaemeka
PY  - 2026
DA  - 2026/01/12
TI  - A Data-Driven Framework for Methane Emission Prediction Using Machine Learning Methods
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 2
IS  - 1
SP  - 53
EP  - 64
DO  - 10.62762/TMI.2025.782852
UR  - https://www.icck.org/article/abs/TMI.2025.782852
KW  - machine learning
KW  - methane emission
KW  - random forest regressor
KW  - prediction
AB  - Greenhouse gas Methane (CH$_4$) has approximately 86 times more impact on global warming than carbon dioxide (CO$_2$) over a 20-year period, and 28 times over a 100-year period. The emission of methane gas into the atmosphere is increasing due to the reliance on fossil-based resources in post-industrial energy consumption, along with the rise in food demand and the generation of organic waste that accompanies a growing human population. CH$_4$ acts as a vital pollutant in the air. The problem addressed in this study was to accurately estimate CH$_4$ emissions from functional urban areas. This study aims to predict CH$_4$ emissions using Time Series (TS) and Machine Learning (ML) models such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor (RFR), and CatBoost Regressor (CABR), etc. The SARIMA model has the best combination of values (1,\,0,\,0)(1,\,1,\,0)$_{12}$. The methane emission data was collected from the World Bank Group from 2019 to 2022. Among all models, the SARIMA model predicted CH$_4$ emissions more accurately than the other models. The results obtained in the study indicate that SARIMA outperforms other techniques. The SARIMA model achieved the most accurate results in terms of R-squared score (R$^2$) = 94%; Root Mean Squared Error (RMSE) = 2.8129; Mean Squared Error (MSE) = 7.9126; Mean Absolute Error (MAE) = 1.8391, among others. This type of prediction enables the government to reduce CH$_4$ emissions at the global level.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Arya2026A,
  author = {Suraj Arya and Anju and Jonas Nnaemeka Onah},
  title = {A Data-Driven Framework for Methane Emission Prediction Using Machine Learning Methods},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2026},
  volume = {2},
  number = {1},
  pages = {53-64},
  doi = {10.62762/TMI.2025.782852},
  url = {https://www.icck.org/article/abs/TMI.2025.782852},
  abstract = {Greenhouse gas Methane (CH\$\_4\$) has approximately 86 times more impact on global warming than carbon dioxide (CO\$\_2\$) over a 20-year period, and 28 times over a 100-year period. The emission of methane gas into the atmosphere is increasing due to the reliance on fossil-based resources in post-industrial energy consumption, along with the rise in food demand and the generation of organic waste that accompanies a growing human population. CH\$\_4\$ acts as a vital pollutant in the air. The problem addressed in this study was to accurately estimate CH\$\_4\$ emissions from functional urban areas. This study aims to predict CH\$\_4\$ emissions using Time Series (TS) and Machine Learning (ML) models such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor (RFR), and CatBoost Regressor (CABR), etc. The SARIMA model has the best combination of values (1,\,0,\,0)(1,\,1,\,0)\$\_{12}\$. The methane emission data was collected from the World Bank Group from 2019 to 2022. Among all models, the SARIMA model predicted CH\$\_4\$ emissions more accurately than the other models. The results obtained in the study indicate that SARIMA outperforms other techniques. The SARIMA model achieved the most accurate results in terms of R-squared score (R\$^2\$) = 94\%; Root Mean Squared Error (RMSE) = 2.8129; Mean Squared Error (MSE) = 7.9126; Mean Absolute Error (MAE) = 1.8391, among others. This type of prediction enables the government to reduce CH\$\_4\$ emissions at the global level.},
  keywords = {machine learning, methane emission, random forest regressor, prediction},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}