A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia.
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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: | Singh, Harikesh1,2 (AUTHOR) Harikesh@research.usc.edu.au, Ang, Li-Minn1 (AUTHOR), Paudyal, Dipak3 (AUTHOR), Acuna, Mauricio4 (AUTHOR), Srivastava, Prashant Kumar1,5 (AUTHOR), Srivastava, Sanjeev Kumar1 (AUTHOR) ssrivast@usc.edu.au |
| Source: | Technology, Knowledge & Learning. Jun2025, Vol. 30 Issue 2, p935-968. 34p. |
| Subject Terms: | *Machine learning, *Artificial intelligence, Artificial neural networks, Support vector machines, Deep learning |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Technology, Knowledge & Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 185423352 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Singh%2C+Harikesh%22">Singh, Harikesh</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> Harikesh@research.usc.edu.au</i><br /><searchLink fieldCode="AR" term="%22Ang%2C+Li-Minn%22">Ang, Li-Minn</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Paudyal%2C+Dipak%22">Paudyal, Dipak</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Acuna%2C+Mauricio%22">Acuna, Mauricio</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Srivastava%2C+Prashant+Kumar%22">Srivastava, Prashant Kumar</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Srivastava%2C+Sanjeev+Kumar%22">Srivastava, Sanjeev Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ssrivast@usc.edu.au</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technology%2C+Knowledge+%26+Learning%22">Technology, Knowledge & Learning</searchLink>. Jun2025, Vol. 30 Issue 2, p935-968. 34p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technology, Knowledge & Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10758-025-09839-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 34 StartPage: 935 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Singh, Harikesh – PersonEntity: Name: NameFull: Ang, Li-Minn – PersonEntity: Name: NameFull: Paudyal, Dipak – PersonEntity: Name: NameFull: Acuna, Mauricio – PersonEntity: Name: NameFull: Srivastava, Prashant Kumar – PersonEntity: Name: NameFull: Srivastava, Sanjeev Kumar IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 22111662 Numbering: – Type: volume Value: 30 – Type: issue Value: 2 Titles: – TitleFull: Technology, Knowledge & Learning Type: main |
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