Comparative Analysis of Wildfire Prediction Models Across Climatic Regions: Béjaïa and Sidi Bel Abbes (Algeria) vs. Montesinho (Portugal)

Climate change and urban expansion are intensifying the risks of wildfires globally, with Mediterranean regions such as Algeria experiencing increased vulnerability due to escalating droughts and heatwaves.These events exacerbate ecological degradation, threaten human safety, and impose substantial economic costs, which requires advanced risk assessment frameworks and adaptive mitigation strategies.This study addresses these challenges through a comparative analysis of wildfire prediction models in three climatically distinct regions: B ja a and Sidi Bel Abbes in Algeria, and Montesinho Natural Park in Portugal.Employing eight machine learning algorithms-CatBoost, XGBoost, Support Vector Machines (SVM), Random Forest (RF), AdaBoost, Logistic Regression (LR), LightGBM, and Decision Trees (DT)-we predict wildfire severity and identify critical drivers using feature selection (Principal Component Analysis (PCA), Genetic Algorithms (GA), Chi-square tests) and explainability techniques (SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), Fuzzy Logic).Key findings reveal stark regional contrasts: In arid Algeria, the temperature and drought indices (Drought Code, DC) dominate the fire dynamics, achieving near-perfect model accuracy (AUC-ROC: 0.97-1.00).In contrast, Portugal's temperate forests prioritize fuel moisture metrics (Duff Moisture Code, DMC) and temporal factors, although complex fire regimes produce a lower discrimination capacity (AUC-ROC: 0.55-0.78).By integrating global (SHAP/PCA) and localized (LIME) insights, we propose region-specific strategies, including IoT-enhanced microclimate regulation, fuel-break networks, and adaptive early warning systems.This work bridges predictive analytics with actionable forest engineering solutions, offering a scalable framework to mitigate wildfire risks in evolving climatic landscapes.