Files and links (1)
Published VersionCC BY V4.0, Open Access
Journal article
Trending and emerging prospects of physics-based and ML-based wildfire spread models: a comprehensive review
Journal of Forestry Research, Vol.35, pp.1-33
2024
Appears in UniSC Supported Open Access Outputs
Abstract
The significant threat of wildfires to forest ecology and biodiversity, particularly in tropical and subtropical regions, underscores the necessity for advanced predictive models amidst shifting climate patterns. There is a need to evaluate and enhance wildfire prediction methods, focusing on their application during extended periods of intense heat and drought. This study reviews various wildfire modelling approaches, including traditional physical, semi-empirical, numerical, and emerging machine learning (ML)-based models. We critically assess these models’ capabilities in predicting fire susceptibility and post-ignition spread, highlighting their strengths and limitations. Our findings indicate that while traditional models provide foundational insights, they often fall short in dynamically estimating parameters and predicting ignition events. Cellular automata models, despite their potential, face challenges in data integration and computational demands. Conversely, ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets, though they encounter interpretability issues. This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths. By incorporating data assimilation techniques with dynamic forecasting models, the predictive capabilities of ML-based predictions can be significantly enhanced. This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications, ultimately contributing to more effective wildfire mitigation and management strategies. Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.
Details
- Title
- Trending and emerging prospects of physics-based and ML-based wildfire spread models: a comprehensive review
- Authors
- Harikesh Singh (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringLi-Minn Ang (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringTom Lewis (Author) - Queensland Forest Consulting Service (Australia)Dipak Paudyal (Author) - APAC Geospatial (Australia)Mauricio Acuna (Author) - University of the Sunshine Coast, Queensland, Forest Research InstitutePrashant Kumar Srivastava (Author) - Banaras Hindu UniversitySanjeev Kumar Srivastava (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Journal of Forestry Research, Vol.35, pp.1-33
- Publisher
- Northeast Forestry University, Chinese Association of Zoological Gardens, Dongbei Linye Daxue
- Date published
- 2024
- DOI
- 10.1007/s11676-024-01783-x
- ISSN
- 1993-0607; 1007-662X
- 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/.
- Grant note
- We thank the University of the Sunshine Coast, Queensland, Australia and SmartSat CRC for their generous support for this PhD research.
- Organisation Unit
- School of Science, Technology and Engineering; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 991066898702621
- Output Type
- Journal article
Metrics
3 File views/ downloads
62 Record Views