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A surrogate model for rapidly assessing the size of a wildfire over time
Citation
KC, U and Aryal, J and Hilton, J and Garg, S, A surrogate model for rapidly assessing the size of a wildfire over time, Fire, 4, (2) Article 20. ISSN 2571-6255 (2021) [Refereed Article]
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Copyright Statement
Copyright 2021 by the authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Abstract
Rapid estimates of the risk from potential wildfires are necessary for operational management and mitigation efforts. Computational models can provide risk metrics, but are typically deterministic and may neglect uncertainties inherent in factors driving the fire. Modeling these uncertainties can more accurately predict risks associated with a particular wildfire, but requires a large number of simulations with a corresponding increase in required computational time. Surrogate models provide a means to rapidly estimate the outcome of a particular model based on implicit uncertainties within the model and are very computationally efficient. In this paper, we detail the development of a surrogate model for the growth of a wildfire based on initial meteorological conditions: temperature, relative humidity, and wind speed. Multiple simulated fires under different conditions are used to develop the surrogate model based on the relationship between the area burnt by the fire and each meteorological variable. The results from nine bio-regions in Tasmania show that the surrogate model can closely represent the change in the size of a wildfire over time. The model could be used for a rapid initial estimate of likely fire risk for operational wildfire management.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | fire spread models, surrogate modeling, sensitivity analysis, global sensitivity analysis, wildfire, cloud computing |
| Research Division: | Information and Computing Sciences |
| Research Group: | Applied computing |
| Research Field: | Applications in physical sciences |
| Objective Division: | Environmental Policy, Climate Change and Natural Hazards |
| Objective Group: | Natural hazards |
| Objective Field: | Climatological hazards (e.g. extreme temperatures, drought and wildfires) |
| UTAS Author: | KC, U (Mr Ujjwal) |
| UTAS Author: | Aryal, J (Dr Jagannath Aryal) |
| UTAS Author: | Garg, S (Dr Saurabh Garg) |
| ID Code: | 146352 |
| Year Published: | 2021 |
| Web of Science® Times Cited: | 1 |
| Deposited By: | Information and Communication Technology |
| Deposited On: | 2021-09-01 |
| Last Modified: | 2021-10-18 |
| Downloads: | 5 View Download Statistics |
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