Deep Learning Prediction of Wildfire Probability Occurrence Using Meteorological Factors from Khenchela Province Forests
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Date
2026
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Abstract
Abstract. Wildfire prediction is crucial for mitigating ecological and economic
losses during forest management. This study presents a three-phased AI-driven
methodology for enhanced wildfire risk assessment in Khenchela Province. First,
explainable AI (XAI, LIME, and SHAP) analyzes eight machine-learning models
(CatBoost, XGBoost, SVM, RF, AdaBoost, LR, LightGBM, and DT) to identify
12 key wildfire predictors from a historical dataset. Second, deep learning models
(LSTM, RNN, and Prophet) forecast meteorological conditions to improve
temporal resolution. Finally, a Bayesian model integrates these forecasts with
moisture codes (FFMC, DMC, and DC) and indices (ISI, BUI, and NDVI) to predict
wildfire likelihood. Validation using Accuracy, Precision, Recall, F1-score,
and AUC demonstrated superior performance, with CatBoost achieving a 95.93%
accuracy. This integrated approach enhances wildfire prediction reliability, enables
proactive mitigation strategies, and shows the synergistic benefits of combining
machine learning, deep learning, and Bayesian modeling for improved wildfire
risk management.