Forest Fire Prediction Using Multi-Source Deep Learning

Forest fire prediction is an important aspect of combating forest fires. This research focuses on the effectiveness of multi-source data (lightning, hydrometric and weather) in the probability prediction of forest fires using deep learning. The results showed that the weather model had the best predictive power (average \(F1 Score = 0.955\) ). The lightning model had an average \(F1 Score = 0.924\) , while the hydrometric model had an average \(F1 Score = 0.690\) . The single-source models were then merged to see the impact of the multi-source data. The multi-source model had an average \(F1 Score = 0.929\) , whereas the average F1Score for the previous three single-source model was 0.856. The results showed that the multi-source model performed similarly to the best-performing single-source model (weather) with a 60% reduction in training data. The multi-source model had a negligible impact from the poor-performing single-source model (hydrometric).