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<p>India’s forests are experiencing quick deterioration, and this causes meaningful threats to ecological strength, climate stability, and biodiversity. This study does introduce one unified multimodal artificial intelligence (AI) framework for forecasts of tree cover loss and carbon emissions across Indian states since it uses past data from 2000 up to 2020. The framework delivers short-term (2021–2025) with long-term (2026–2030) projections for it leverages a comparative modeling approach that includes Linear Regression, Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks. Model performance was evaluated using R², Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Robustness was also ensured by expanding-window cross-validation and hyperparameter tuning via GridSearchCV and Optuna. Results indicate that the nation is projecting less tree cover loss though regional volatility remains important, particularly in the North-Eastern and Central regions. By contrast, carbon emissions continue to rise. This suggests that the dynamics of forest loss and emissions are decoupled because of legacy degradation with anthropogenic drivers at play. SHAP-based explainable AI analysis finds biomass stock, canopy extent, and lagged tree loss are dominant predictive features. Spatial volatility assessment additionally identifies emission together with deforestation hotspots such as Mizoram, Tripura, and Odisha. This identification does support the targeted prioritization that exists in forest management. Through forecasting advanced time-series, analyzing spatial data, and inferring causal relationships within a single interpretable and scalable architecture, this framework offers actionable perceptions for forest dashboards, climate mitigation strategies, carbon offset programs, and adaptive policy planning. India’s commitments that are made under global environmental and climate agreements do receive meaningful contributions along with a strong foundation for data-driven forest governance.</p>

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Multimodal AI framework for forecasting tree cover loss and carbon emissions in india: integrated time-series modeling, spatial volatility mapping, and explainable causal analysis

  • 期刊论文

  • 发表日期：

    2025年12月19日

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  • Priyam Nath Bhowmik,
  • Kezia Saini,
  • Pradyut Anand,
  • Tagore Sai Priya Nunna

摘要

India’s forests are experiencing quick deterioration, and this causes meaningful threats to ecological strength, climate stability, and biodiversity. This study does introduce one unified multimodal artificial intelligence (AI) framework for forecasts of tree cover loss and carbon emissions across Indian states since it uses past data from 2000 up to 2020. The framework delivers short-term (2021–2025) with long-term (2026–2030) projections for it leverages a comparative modeling approach that includes Linear Regression, Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks. Model performance was evaluated using R², Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Robustness was also ensured by expanding-window cross-validation and hyperparameter tuning via GridSearchCV and Optuna. Results indicate that the nation is projecting less tree cover loss though regional volatility remains important, particularly in the North-Eastern and Central regions. By contrast, carbon emissions continue to rise. This suggests that the dynamics of forest loss and emissions are decoupled because of legacy degradation with anthropogenic drivers at play. SHAP-based explainable AI analysis finds biomass stock, canopy extent, and lagged tree loss are dominant predictive features. Spatial volatility assessment additionally identifies emission together with deforestation hotspots such as Mizoram, Tripura, and Odisha. This identification does support the targeted prioritization that exists in forest management. Through forecasting advanced time-series, analyzing spatial data, and inferring causal relationships within a single interpretable and scalable architecture, this framework offers actionable perceptions for forest dashboards, climate mitigation strategies, carbon offset programs, and adaptive policy planning. India’s commitments that are made under global environmental and climate agreements do receive meaningful contributions along with a strong foundation for data-driven forest governance.