Using machine learning models to forecast methane emissions from agriculture in India

Methane (CH4) is a potent and powerful greenhouse gas with significant warming potential. CH4 has approximately 84 times the global warming potential (GWP) of CO2 over a 20-year period and 25 times over a 100-year period. Methane persists in the atmosphere for approximately a decade before breaking down through oxidation processes. In order to forecast methane emissions from agricultural and related activities, this study applied an evaluation approach. This study analyzed annual data from 1990 to 2021, collected from the FAOSTAT website, using 7 machine learning models: Random Forest, LASSO, Gradient Boosting, AdaBoost, XGB, Ridge Regression and Linear Regression. The results indicate that the Linear Regression model outperformed the other models in predicting methane emissions. The results show that linear regression is more effective than the various machine learning algorithms. The linear regression model (R² = 0.98, RMSE = 1.95, MSE = 3.86 and MAE = 1.45) achieved the best performance among all models in terms of accuracy. The comparative study's findings should yield a highly accurate assessment of the methane emissions from agricultural areas and help with the creation of laws or other policies aimed at reducing those emissions. These findings can assist the Indian government in formulating effective policies to mitigate methane emissions while maintaining agricultural productivity. Our approach estimates methane emissions from agriculture in India with an R² value of 0.99, indicating high predictive accuracy.