AI Driven Wildfire Prediction in Australia Using Machine Learning for Effective Disaster Prevention

Wildfires pose significant ecological, economic, and societal challenges in Australia, with their frequency and intensity exacerbated by climate change. Accurate wildfire prediction is critical for effective fire management strategies. This study investigates the performance of machine learning models incorporating time-series data for wildfire prediction across seven Australian regions. Comparisons between models with and without SMOTE and ANOVA revealed that these techniques did not consistently improve performance. Without SMOTE and ANOVA, CatBoost achieved the highest overall accuracy (84.35 %), followed closely by HXGBoost (84.32%}) and LightGBM (84.16%). After applying SMOTE and ANOVA, all models experienced slight declines in accuracy, sensitivity, and F1 scores, indicating that oversampling and feature selection may have introduced noise rather than meaningful improvements. Regionspecific models significantly outperformed a single model for all of Australia, with exceptional accuracies observed in Queensland (96.36%) and Western Australia (97.95%). However, challenges persisted in regions like South Australia, where accuracy dropped further after applying SMOTE and ANOVA (from 70.16% to (65.03%), highlighting the need for more targeted feature selection and data-balancing techniques.