Research Article
Forest Fire Prediction Using Multi-Source Deep Learning
@INPROCEEDINGS{10.1007/978-3-031-52265-9_9, author={Abdul Mutakabbir and Chung-Horng Lung and Samuel A. Ajila and Marzia Zaman and Kshirasagar Naik and Richard Purcell and Srinivas Sampalli}, title={Forest Fire Prediction Using Multi-Source Deep Learning}, proceedings={Big Data Technologies and Applications. 13th EAI International Conference, BDTA 2023, Edinburgh, UK, August 23-24, 2023, Proceedings}, proceedings_a={BDTA}, year={2024}, month={1}, keywords={Deep Learning Multi-Modal Multi-Source Data Big Data Big Data Analysis Binary Classification Forest Fires}, doi={10.1007/978-3-031-52265-9_9} }- Abdul Mutakabbir
Chung-Horng Lung
Samuel A. Ajila
Marzia Zaman
Kshirasagar Naik
Richard Purcell
Srinivas Sampalli
Year: 2024
Forest Fire Prediction Using Multi-Source Deep Learning
BDTA
Springer
DOI: 10.1007/978-3-031-52265-9_9
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 averageF1Scorefor 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).
