Integrating remote sensing and machine learning for methane emission prediction in Konya

Abstract

Methane (CH₄) is a potent greenhouse gas influenced by various environmental factors. This study employs remote sensing data and machine learning techniques to analyze the relationship between CH₄ concentration and key environmental variables in Konya, Türkiye. Using datasets from MODIS, CHIRPS, NASA FLDAS, Copernicus Sentinel-5P, and Landsat 8, we developed regression models to predict CH₄ distribution. Four machine learning models—Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Trees (GBT), and Classification and Regression Trees (CART)—were trained and evaluated based on R, R², MAE, and RMSE metrics. The results indicate that GBT achieved the highest accuracy (R = 0.89, R² = 0.78, MAE = 2.574, RMSE = 4.16), while SVM exhibited poor predictive performance. The findings highlight the effectiveness of tree-based ensemble models in methane estimation, suggesting that integrating diverse environmental factors enhances predictive accuracy. These insights contribute to improving methane monitoring strategies and guiding mitigation policies.

Keywords

• Methane
• Sentinel-5P
• Machine learning

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