Browsing by Author "Mohamed, Lamri"

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    Deep Learning Prediction of Wildfire Probability Occurrence Using Meteorological Factors from Khenchela Province Forests
    (2026) Mohamed, Lamri
    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.