A Machine Learning Framework for AI-Based Wildfire Risk Assessment

Forest fires pose a severe risk to both the environment and human life, often causing extensive damage to ecosystems and property. With the growing impact of climate change, including rising temperatures and prolonged droughts, the frequency and intensity of forest fires have increased significantly. Effective early detection and intervention are crucial to minimizing these impacts. In recent years, machine learning (ML) techniques have been applied to forest fire prediction to improve early warning systems. This study aims to reduce the risk and impact of forest fires by predicting their occurrence ahead of time using machine learning. A dataset, sourced from NASA's Oak Ridge National Laboratory (ORNL), containing detailed environmental and forest-related factors, was used for this purpose. Preprocessing steps were applied to prepare the data for classification, including feature selection techniques that narrowed down the dataset from 35 to the most relevant features. Various machine learning algorithms—Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbor (K-NN), and Naive Bayes (NB)—were employed for classification. The performance of the models was evaluated, and hyperparameter optimization was performed to select the best parameters. The Random Forest model achieved an impressive accuracy of 97%, closely followed by Naive Bayes at 96%, highlighting the effectiveness of these algorithms in predicting forest fires.