Adaptive Hybrid Stacking Ensemble Learning for Real-Time Forest Fire Risk Prediction

Abstract

Forest and land fires remain a recurring environmental issue, particularly in regions with high climate variability such as Indonesia. This study proposes an Adaptive Hybrid Stacking Ensemble approach for real-time fire risk prediction by integrating meteorological data and IoT-based sensing systems. The model combines multiple base learners with a rotation strategy and an exponential accuracy weighting mechanism to optimize the contribution of each model. A dataset consisting of 4,931 meteorological records was used, including variables such as Average Temperature, Relative Humidity, Rainfall, Wind Speed, and Soil Surface Moisture. Experimental results show that the proposed model significantly outperforms conventional stacking, achieving an accuracy of up to 92.47%, compared to 86%87% in baseline stacking. The optimal performance was obtained at α= 5 in the weighting scheme, which effectively balances model contribution. Evaluation using confusion matrix, ROC curve, and K-fold cross-validation confirms the model's robustness, with AUC values reaching 0.981.00 and stable accuracy across folds. In addition, Explainable AI using SHAP reveals that Rainfall and Soil Surface Moisture are the most influential features in fire risk prediction. The integration with IoT enables real-time data processing and prediction, making the system applicable for early warning and disaster mitigation.