A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions

Ahmad Said; Obada Al-khatib; Abeer Elkhouly; Zina Abohaia; Mai El Barachi

doi:10.33904/ejfe.1606577

A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions

Abstract

Wildfires pose escalating environmental, social, and economic challenges worldwide, intensified by climate change, prolonged droughts, and expanding human activity. This study reviews the evolution of wildfire management from traditional detection, control, and prevention methods to advanced, data-driven approaches. It highlights the limitations of historical practices, such as reliance on ground-based monitoring, satellite imaging, and fire weather indices, and explores current global and regional strategies, including the integration of remote sensing, advanced analytics, and predictive modelling. Special attention is given to the role of machine learning (ML), particularly ensemble methods such as Random Forest (RF) and XGBoost, which have demonstrated superior predictive performance by capturing complex, non-linear relationships in wildfire data. The study synthesizes key data sources, including meteorological, vegetation, land-type, and satellite datasets, and outlines methods for their integration to improve predictive accuracy. Despite significant progress, persistent challenges remain in data fusion, spatial-temporal modelling, model generalizability, and interpretability. Future research emphasizes multi-source data integration, advanced feature engineering, spatio-temporal analysis, and enhanced model explainability using SHAP and LIME to ensure transparent and actionable predictions. Ultimately, this review underscores the potential of ML-driven systems to transform wildfire prediction into a proactive and sustainable component of modern disaster management.

Keywords

Linear Regression, Machine Learning, Random Forest, Wildfire, XGBoost, Australia

Supporting Institution

The authors declared that this study has received no financial support.

Ethical Statement

N/A.

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Details

Primary Language

English

Subjects

Information Systems (Other), Forestry Sciences (Other)

Journal Section

Review

Authors

Ahmad Said
0009-0004-4510-2534
United Arab Emirates

Obada Al-khatib ^*
0000-0001-9473-2365
United Arab Emirates

Abeer Elkhouly
0000-0001-5142-379X
United Arab Emirates

Zina Abohaia This is me
0009-0008-6397-0553
United Arab Emirates

Mai El Barachi This is me
0000-0003-1193-6982
United Arab Emirates

Early Pub Date

June 11, 2026

Publication Date

June 11, 2026

Submission Date

December 25, 2024

Acceptance Date

December 3, 2025

Published in Issue

Year 2026 Volume: 12 Number: 1

DOI

https://doi.org/10.33904/ejfe.1606577

IZ

https://izlik.org/JA64EG36YL

APA

Said, A., Al-khatib, O., Elkhouly, A., Abohaia, Z., & El Barachi, M. (2026). A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions. European Journal of Forest Engineering, 12(1), 90-105. https://doi.org/10.33904/ejfe.1606577

AMA

1.Said A, Al-khatib O, Elkhouly A, Abohaia Z, El Barachi M. A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions. Eur J Forest Eng. 2026;12(1):90-105. doi:10.33904/ejfe.1606577

Chicago

Said, Ahmad, Obada Al-khatib, Abeer Elkhouly, Zina Abohaia, and Mai El Barachi. 2026. “A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions”. European Journal of Forest Engineering 12 (1): 90-105. https://doi.org/10.33904/ejfe.1606577.

EndNote

Said A, Al-khatib O, Elkhouly A, Abohaia Z, El Barachi M (June 1, 2026) A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions. European Journal of Forest Engineering 12 1 90–105.

IEEE

[1]A. Said, O. Al-khatib, A. Elkhouly, Z. Abohaia, and M. El Barachi, “A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions”, Eur J Forest Eng, vol. 12, no. 1, pp. 90–105, June 2026, doi: 10.33904/ejfe.1606577.

ISNAD

Said, Ahmad - Al-khatib, Obada - Elkhouly, Abeer - Abohaia, Zina - El Barachi, Mai. “A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions”. European Journal of Forest Engineering 12/1 (June 1, 2026): 90-105. https://doi.org/10.33904/ejfe.1606577.

JAMA

1.Said A, Al-khatib O, Elkhouly A, Abohaia Z, El Barachi M. A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions. Eur J Forest Eng. 2026;12:90–105.

MLA

Said, Ahmad, et al. “A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions”. European Journal of Forest Engineering, vol. 12, no. 1, June 2026, pp. 90-105, doi:10.33904/ejfe.1606577.

Vancouver

1.Ahmad Said, Obada Al-khatib, Abeer Elkhouly, Zina Abohaia, Mai El Barachi. A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions. Eur J Forest Eng. 2026 Jun. 1;12(1):90-105. doi:10.33904/ejfe.1606577

Creative Commons License

The works published in European Journal of Forest Engineering (EJFE) are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

A Review of Machine Learning Approaches for Wildfire Prediction and Management in Australia: Challenges, Data Integration and Future Directions

Abstract

Keywords

Supporting Institution

Ethical Statement

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite