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Journals / Türkiye Uzaktan Algılama Dergisi / 2025 / Volume: 7 Issue: 2

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

Nehir Uyar(ZONGULDAK BÜLENT ECEVİT ÜNİVERSİTESİ, ZONGULDAK MESLEK YÜKSEKOKULU)0000-0003-3358-3145
Pages
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Publication Date
2025-12-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Methane (CH₄) is a potent greenhouse gas influenced by various environmental factors.
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Key Findings & Data:Using datasets from MODIS, CHIRPS, NASA FLDAS, Copernicus Sentinel-5P, and Landsat 8, we developed regression models to predict CH₄ distribution.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Methane · Sentinel-5P · Machine learning

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.

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Cite This Article

Uyar, N. (2025). Integrating remote sensing and machine learning for methane emission prediction in Konya. Türkiye Uzaktan Algılama Dergisi, Volume: 7 Issue: 2. https://doi.org/10.51489/tuzal.1670906

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