Nuthur: An Intelligent Wildfire Prediction and Early Warning System for the Al-Soudah Region

Forest fires remain a major threat to biodiversity, human settlements, and the climate. This study presents Nuthur, an intelligent wildfire prediction and early-warning system for Al-Soudah, Saudi Arabia, integrating near-real-time environmental data with artificial intelligence models. The system used the Algerian Forest Fire dataset and a newly created local Saudi Arabian dataset. L1 regularization and Recursive Feature Elimination with Cross-Validation (RFECV) were used to examine relevant environmental variables, while oversampling, undersampling, and (Conditional Tabular Generative Adversarial Network) CTGAN-based synthetic augmentation were evaluated to address class imbalance. Multiple ML and DL models were evaluated, including Random Forest (RF), SVM, XGBoost, CatBoost, ensemble models, MLP, TabNet, and exploratory LSTM and CNN models, which were not interpreted as temporal or spatial models. Under random five-fold cross-validation, ML models achieved accuracy values from 0.89 to 0.97, with XGBoost with oversampling achieving the highest accuracy of 0.97. Deep-learning models achieved accuracy values from 0.75 to 0.94, with TabNet using RFECV achieving the best deep-learning result. A separate spatial cross-validation analysis of the Saudi dataset showed lower geographic generalization performance. CatBoost without oversampling achieved the highest mean spatial accuracy (0.8317) and weighted F1-score (0.7782), while logistic regression with oversampling achieved the highest fire-class recall (0.5616). In contrast, XGBoost with oversampling had a fire-class recall of 0.1096. These results highlight the need for further geographically and temporally diverse Saudi data before operational deployment.