Understanding green house gases emission dynamics from forest fires in Thailand using predictive models

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dc.contributor.authorShahzad, Fahad
dc.contributor.authorMehmood, Kaleem
dc.contributor.authorAnees, Shoaib Ahmad
dc.contributor.authorAdnan, Muhammad
dc.contributor.authorHussain, Khadim
dc.contributor.authorKhan, Waseem Razzaq
dc.contributor.authorShah, Munawar
dc.contributor.authorJamjareegulgarn, Punyawi
dc.contributor.authorOliveira, Manuela
dc.contributor.authorBorges, José G.
dc.date.accessioned2026-08-06T10:54:38Z
dc.date.available2026-08-06T10:54:38Z
dc.date.issued2026-02-01
dc.description.abstractForest fires are a major driver of carbon emissions, particularly in tropical regions where climate variability and land use practices intensify their frequency and impact. This study investigates the spatiotemporal trends and emission dynamics of forest fires across Thailand's three dominant vegetation types- Evergreen Broadleaf Forest (EBF), Deciduous Broadleaf Forest (DBF), and Grassland over three climatic seasons (Dry, Hot, and Wet) in the period 2001–2023. Using the Mann-Kendall trend test and Sen's Slope estimator, we observed significant declines in burnt area during the Dry season in EBF and Grasslands, with no consistent trend in DBF. Fire–vegetation interactions revealed seasonally specific effects: positive correlations between fire count and Net Primary Productivity (NPP) were detected in the Wet and the hot seasons in the case of DBF and Grasslands, respectively. Emission analysis showed that CO₂ was the dominant greenhouse gas released, with the Dry season contributing to most emissions, although Hot season emissions have increased over time. Machine learning models Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) explained over 78 % of the variance in CO₂ emissions on test data (R<sup>2</sup> = 0.79 for RF, 0.78 for XGBoost), despite higher Root Mean Square Error (RMSE) values (∼550) on unseen data. The Shapley Additive Explanations (SHAP) analysis identified wind components and solar radiation as key predictive variables. Central, Northeastern, and Northern Thailand emerged as emission hotspots. These findings improve our understanding of emission dynamics from tropical fires and underscore the need for region-specific mitigation strategies to inform carbon inventories and climate policy.
dc.identifier.citationGlobal and Planetary Change, 257, 2026
dc.identifier.doi10.1016/j.gloplacha.2025.105236
dc.identifier.issn09218181
dc.identifier.other2-s2.0-105024558968
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17882
dc.sourceGlobal and Planetary Change
dc.subjectFire management strategies
dc.subjectForest fires
dc.subjectGreenhouse gas emissions
dc.subjectMachine learning
dc.subjectNet primary productivity
dc.titleUnderstanding green house gases emission dynamics from forest fires in Thailand using predictive models
dc.typeArticle

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