A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia

Harikesh Singh; Li-Minn Ang; Dipak Paudyal; Mauricio Acuna; Prashant Kumar Srivastava; Sanjeev Kumar Srivastava

doi:10.1007/s10758-025-09839-5

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A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia

Journal article

Open access

Peer reviewed

A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia

Harikesh Singh, Li-Minn Ang, Dipak Paudyal, Mauricio Acuna, Prashant Kumar Srivastava and Sanjeev Kumar Srivastava

Technology, Knowledge and Learning, Vol.30, pp.935-968

2025

DOI: https://doi.org/10.1007/s10758-025-09839-5

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Abstract

wildfire simulators

machine learning (ML)

wildfire prediction

deep learning (DL)

artificial intelligence (AI)

Wildfires pose significant environmental threats in Australia, impacting ecosystems, human lives, and property. This review article provides a comprehensive analysis of various empirical and dynamic wildfire simulators alongside machine learning (ML) techniques employed for wildfire prediction in Australia. The study examines the effectiveness of traditional empirical methods, dynamic physical models, and advanced ML algorithms in forecasting wildfire spread and behaviour. Key simulators discussed include PHOENIX Rapidfire, SPARK, AUSTRALIS, REDEYE, and IGNITE, each evaluated for their inputs, models, and outputs. Additionally, the application of ML methods such as artificial neural networks, logistic regression, decision trees, and support vector machines is explored, highlighting their predictive capabilities and limitations. The integration of these advanced techniques is essential for enhancing the accuracy of wildfire predictions, enabling better preparedness and response strategies. This review aims to inform future research and development in wildfire prediction and management, ultimately contributing to more effective fire mitigation efforts in Australia and beyond.

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Title: A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia
Authors: Harikesh Singh (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Li-Minn Ang - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Dipak Paudyal - APAC Geospatial (Australia)
Mauricio Acuna - University of the Sunshine Coast, Queensland, Forest Research Institute
Prashant Kumar Srivastava - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Sanjeev Kumar Srivastava - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Publication details: Technology, Knowledge and Learning, Vol.30, pp.935-968
Publisher: Springer Dordrecht
Date published: 2025
DOI: 10.1007/s10758-025-09839-5
ISSN: 2211-1670; 2211-1662
Copyright note: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Data Availability: Data will be available on request.
Grant note: We would like to thank the University of the Sunshine Coast, Queensland, Australia and SmartSat CRC for their generous support for this Ph.D. research.
Organisation Unit: Forest Industries Research Centre; School of Science, Technology and Engineering; Engage Research Lab; Sustainability Research Cluster
Language: English
Record Identifier: 991119282302621
Output Type: Journal article

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