ICCK Transactions on Machine Intelligence
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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 86 times more impact on global warming than carbon dioxide (CO$_2$). 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). The methane emission data was collected from the World Bank’s 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 performed 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, etc. 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 86 times more impact on global warming than carbon dioxide (CO\$\_2\$). 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). The methane emission data was collected from the World Bank’s 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 performed 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, etc. 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}
}
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