Multi‐Horizon Active Layer Thickness Prediction Across Circum‐Arctic Permafrost Regions Using Geospatial Machine Learning

Abstract Accurately predicting active layer thickness (ALT) is critical for assessing permafrost stability and mitigating risks to Arctic infrastructure, ecosystems, and communities. However, large‐scale ALT prediction remains challenging due to limited in situ observations and high spatial variability in environmental drivers. This study introduces a data‐driven framework for maximum annual ALT prediction up to +5 years in the future that combines field measurements with geospatial features. First, key ALT predictors are identified using a robust multi‐stage feature selection strategy from a large set of 80 geospatial features. Second, a weighted ensemble model of CatBoost, Extremely Randomized Trees, and Bagging regressors is trained separately at each prediction horizon. Accordingly, the proposed prediction model is evaluated across 115 sites from the Circumpolar Active Layer Monitoring network. Results highlight seven essential drivers of ALT namely geographic, seasonal, and vegetation‐related features across the Arctic. In addition, model performance consistently outperforms baseline models and demonstrates strong predictive accuracy and generalization across