Bushfire Susceptibility Mapping Using Gene Expression Programming and Machine Learning Methods: A Case Study of Kangaroo Island, South Australia

Maryamsadat Hosseini, Samsung Lim

2023

Abstract

Kangaroo Island, South Australia is one of the bushfire-prone areas. A catastrophic bushfire known as the black summer hit Kangaroo Island in 2019/2020. We chose Kangaroo Island as a case study to generate bushfire susceptibility maps using five different methods, namely gene expression programming (GEP), random forest (RF), support vector machine (SVM), frequency ratio (FR) and logistic regression (LR). To generate bushfire susceptibility maps, we used eight contributing factors including: digital elevation model, slope, aspect, normalized difference vegetation index, distance to roads, distance to streams, precipitation, and land cover. The proposed methods were evaluated by area under the curves (AUCs) of receiver operating characteristic. RF performed best with an AUC of 0.93, followed by SVM and GEP with AUCs equal to 0.89 and 0.88, respectively, but LR and FR performed least among the five methods with AUCs 0.85 and 0.84, respectively. The generated bushfire susceptibility maps show that western and central areas of Kangaroo Island are highly vulnerable to bushfire.

Paper Citation

in Harvard Style

Hosseini M. and Lim S. (2023). Bushfire Susceptibility Mapping Using Gene Expression Programming and Machine Learning Methods: A Case Study of Kangaroo Island, South Australia. In Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-649-1, SciTePress, pages 123-127. DOI: 10.5220/0011724700003473

in Bibtex Style

@conference{gistam23,
author={Maryamsadat Hosseini and Samsung Lim},
title={Bushfire Susceptibility Mapping Using Gene Expression Programming and Machine Learning Methods: A Case Study of Kangaroo Island, South Australia},
booktitle={Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2023},
pages={123-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011724700003473},
isbn={978-989-758-649-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Bushfire Susceptibility Mapping Using Gene Expression Programming and Machine Learning Methods: A Case Study of Kangaroo Island, South Australia
SN - 978-989-758-649-1
AU - Hosseini M.
AU - Lim S.
PY - 2023
SP - 123
EP - 127
DO - 10.5220/0011724700003473
PB - SciTePress