About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Big Data Technologies and Applications. 13th EAI International Conference, BDTA 2023, Edinburgh, UK, August 23-24, 2023, Proceedings

Research Article

Forest Fire Prediction Using Multi-Source Deep Learning

Cite
BibTeX Plain Text
  • @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
Abdul Mutakabbir1,*, Chung-Horng Lung1, Samuel A. Ajila1, Marzia Zaman2, Kshirasagar Naik3, Richard Purcell4, Srinivas Sampalli4
  • 1: Department of Systems and Computer Engineering, Carleton University
  • 2: Research and Development, Cistel Technology
  • 3: Department of Electrical and Computer Engineering, University of Waterloo
  • 4: Faculty of Computer Science, Dalhousie University
*Contact email: mutakabbir@cmail.carleton.ca

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).

Keywords
Deep Learning Multi-Modal Multi-Source Data Big Data Big Data Analysis Binary Classification Forest Fires
Published
2024-01-31
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-52265-9_9
Copyright © 2023–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL