Robust Prediction of Wildfire Spread in Australia
<p dir="ltr">Wildfires can have devastating effects on urban infrastructure and natural ecosystems, making wildfire management an important, but yet complex and difficult task. The systematic collection of data, increased computing power, and the development of physical models made i...
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| منشور في: |
2025
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| _version_ | 1864513531471200256 |
|---|---|
| author | Michael Palk (17947841) |
| author2 | Katharina Knappmann (22928317) Stefan Voss (17947844) Raka Jovanovic (17947838) |
| author2_role | author author author |
| author_facet | Michael Palk (17947841) Katharina Knappmann (22928317) Stefan Voss (17947844) Raka Jovanovic (17947838) |
| author_role | author |
| dc.creator.none.fl_str_mv | Michael Palk (17947841) Katharina Knappmann (22928317) Stefan Voss (17947844) Raka Jovanovic (17947838) |
| dc.date.none.fl_str_mv | 2025-07-31T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3592124 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Robust_Prediction_of_Wildfire_Spread_in_Australia/30971335 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Ecology Environmental sciences Climate change impacts and adaptation Ecological applications Environmental management Predictive analytics time series forecasting deep learning transformers robust prediction Huber loss Australian wildfires Data models Computational modeling Forests Time series analysis Machine learning Deep learning |
| dc.title.none.fl_str_mv | Robust Prediction of Wildfire Spread in Australia |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Wildfires can have devastating effects on urban infrastructure and natural ecosystems, making wildfire management an important, but yet complex and difficult task. The systematic collection of data, increased computing power, and the development of physical models made it possible to get an understanding of the dynamics of wildfire spread. As exact computational simulations of wildfires are not feasible yet, several subtasks such as the estimation of the spread rate were analyzed with various methods in the literature. In this paper, different types of predictive models are evaluated to forecast the spread of wildfires on a daily and weekly basis in a comparative study. These models are tested on real-world data of wildfires from the seven Australian regions New South Wales, Northern Territory, Queensland, South Australia, Tasmania, Victoria, and Western Australia from 2005 to 2020, including weather, vegetation, and land cover class data, in a univariate and multivariate setting. Furthermore, relevant features are identified and discussed which can have an important influence on wildfire spread. We find that robust models, which are less sensitive to outliers, capture the dynamics of wildfire spread most accurately.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3592124" target="_blank">https://dx.doi.org/10.1109/access.2025.3592124</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c2869c2b7f5662adeada7f025d6e3a83 |
| identifier_str_mv | 10.1109/access.2025.3592124 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30971335 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Robust Prediction of Wildfire Spread in AustraliaMichael Palk (17947841)Katharina Knappmann (22928317)Stefan Voss (17947844)Raka Jovanovic (17947838)Biological sciencesEcologyEnvironmental sciencesClimate change impacts and adaptationEcological applicationsEnvironmental managementPredictive analyticstime series forecastingdeep learningtransformersrobust predictionHuber lossAustralian wildfiresData modelsComputational modelingForestsTime series analysisMachine learningDeep learning<p dir="ltr">Wildfires can have devastating effects on urban infrastructure and natural ecosystems, making wildfire management an important, but yet complex and difficult task. The systematic collection of data, increased computing power, and the development of physical models made it possible to get an understanding of the dynamics of wildfire spread. As exact computational simulations of wildfires are not feasible yet, several subtasks such as the estimation of the spread rate were analyzed with various methods in the literature. In this paper, different types of predictive models are evaluated to forecast the spread of wildfires on a daily and weekly basis in a comparative study. These models are tested on real-world data of wildfires from the seven Australian regions New South Wales, Northern Territory, Queensland, South Australia, Tasmania, Victoria, and Western Australia from 2005 to 2020, including weather, vegetation, and land cover class data, in a univariate and multivariate setting. Furthermore, relevant features are identified and discussed which can have an important influence on wildfire spread. We find that robust models, which are less sensitive to outliers, capture the dynamics of wildfire spread most accurately.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3592124" target="_blank">https://dx.doi.org/10.1109/access.2025.3592124</a></p>2025-07-31T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3592124https://figshare.com/articles/journal_contribution/Robust_Prediction_of_Wildfire_Spread_in_Australia/30971335CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/309713352025-07-31T09:00:00Z |
| spellingShingle | Robust Prediction of Wildfire Spread in Australia Michael Palk (17947841) Biological sciences Ecology Environmental sciences Climate change impacts and adaptation Ecological applications Environmental management Predictive analytics time series forecasting deep learning transformers robust prediction Huber loss Australian wildfires Data models Computational modeling Forests Time series analysis Machine learning Deep learning |
| status_str | publishedVersion |
| title | Robust Prediction of Wildfire Spread in Australia |
| title_full | Robust Prediction of Wildfire Spread in Australia |
| title_fullStr | Robust Prediction of Wildfire Spread in Australia |
| title_full_unstemmed | Robust Prediction of Wildfire Spread in Australia |
| title_short | Robust Prediction of Wildfire Spread in Australia |
| title_sort | Robust Prediction of Wildfire Spread in Australia |
| topic | Biological sciences Ecology Environmental sciences Climate change impacts and adaptation Ecological applications Environmental management Predictive analytics time series forecasting deep learning transformers robust prediction Huber loss Australian wildfires Data models Computational modeling Forests Time series analysis Machine learning Deep learning |