Robust Prediction of Wildfire Spread in Australia
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 understa...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11095684/ |
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| author | Michael Palk Katharina Knappmann Stefan Voss Raka Jovanovic |
| author_facet | Michael Palk Katharina Knappmann Stefan Voss Raka Jovanovic |
| author_sort | Michael Palk |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-992c1a6348ba4540bb20cffd3941f15a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-992c1a6348ba4540bb20cffd3941f15a2025-08-20T03:58:39ZengIEEEIEEE Access2169-35362025-01-011313270313272310.1109/ACCESS.2025.359212411095684Robust Prediction of Wildfire Spread in AustraliaMichael Palk0https://orcid.org/0000-0001-7463-6827Katharina Knappmann1Stefan Voss2https://orcid.org/0000-0003-1296-4221Raka Jovanovic3https://orcid.org/0000-0001-8167-1516Institute of Information Systems, University of Hamburg, Hamburg, GermanyInstitute of Information Systems, University of Hamburg, Hamburg, GermanyInstitute of Information Systems, University of Hamburg, Hamburg, GermanyQatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, QatarWildfires 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.https://ieeexplore.ieee.org/document/11095684/Predictive analyticstime series forecastingdeep learningtransformersrobust predictionHuber loss |
| spellingShingle | Michael Palk Katharina Knappmann Stefan Voss Raka Jovanovic Robust Prediction of Wildfire Spread in Australia IEEE Access Predictive analytics time series forecasting deep learning transformers robust prediction Huber loss |
| 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 | Predictive analytics time series forecasting deep learning transformers robust prediction Huber loss |
| url | https://ieeexplore.ieee.org/document/11095684/ |
| work_keys_str_mv | AT michaelpalk robustpredictionofwildfirespreadinaustralia AT katharinaknappmann robustpredictionofwildfirespreadinaustralia AT stefanvoss robustpredictionofwildfirespreadinaustralia AT rakajovanovic robustpredictionofwildfirespreadinaustralia |