The Impact of Artificial Intelligence in Predicting Forest Fires Using Spatio- Temporal Data Mining

The FILINFOR approach focuses on predicting forest fires through the application of Artificial Intelligence (AI), Spatio- Temporal Data Mining (STDM), and Machine Learning (ML) algorithms. Forest fire prevention entails the extraction of association rules and the utilization of Artificial Neural Networks (ANN). Furthermore, this method harnesses a spatio-temporal dataset to bolster fire prediction and discrimination capabilities. FILINFOR aims to mitigate damage, encompassing human, physical, animal, and material losses. Notably, the proposed approach demonstrates promising performance metrics, boasting an accuracy of 93.5%, an error rate of 0.0712, a recall of 92.5%, and a precision of 93%. These findings underscore the potential for effective natural risk prevention. Future endeavors will strive to refine wildfire prediction accuracy within the dataset.