Forest Fire Prediction Using Multi-Source Deep Learning

Abstract

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).