Big Data Synthesis and Class Imbalance Rectification for Enhanced Forest Fire Classification Modeling

Fatemeh Tavakoli, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

2024

Abstract

Forest fires have been escalating in frequency and intensity across Canada in recent times. This study employs machine learning techniques and builds a dataset framework utilizing Copernicus climate reanalysis data combined with historical fire data to develop a fire classification framework. Three algorithms, Random Forest, XGBoost, and LightGBM, were evaluated. Given the pronounced class imbalance of 154:1 between “non-fire” and “fire” events, we rigorously employed two re-sampling strategies: Spatiotemporal, focusing on spatial and seasonal considerations, and Technique-Driven, leveraging advanced algorithmic approaches. Ultimately, XGBoost combined with NearMiss Version 3 in a 0.09 sampling ratio between “non-fire” and “fire” events yielded the best results: 98.08% precision, 86.06% sensitivity, and 93.03% specificity.

Paper Citation

in Harvard Style

Tavakoli F., Naik K., Zaman M., Purcell R., Sampalli S., Mutakabbir A., Lung C. and Ravichandran T. (2024). Big Data Synthesis and Class Imbalance Rectification for Enhanced Forest Fire Classification Modeling. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 264-275. DOI: 10.5220/0012363000003636

in Bibtex Style

@conference{icaart24,
author={Fatemeh Tavakoli and Kshirasagar Naik and Marzia Zaman and Richard Purcell and Srinivas Sampalli and Abdul Mutakabbir and Chung-Horng Lung and Thambirajah Ravichandran},
title={Big Data Synthesis and Class Imbalance Rectification for Enhanced Forest Fire Classification Modeling},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={264-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012363000003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Big Data Synthesis and Class Imbalance Rectification for Enhanced Forest Fire Classification Modeling
SN - 978-989-758-680-4
AU - Tavakoli F.
AU - Naik K.
AU - Zaman M.
AU - Purcell R.
AU - Sampalli S.
AU - Mutakabbir A.
AU - Lung C.
AU - Ravichandran T.
PY - 2024
SP - 264
EP - 275
DO - 10.5220/0012363000003636
PB - SciTePress