Attention-based wildland fire spread modeling using fire-tracking satellite observations

Zou, Yufei, Sadeghi, Mojtaba, Liu, Yaling, Puchko, Alexandra, Le, Son, Chen, Yang, Andela, Niels ORCID: https://orcid.org/0000-0002-8241-6143 and Gentine, Pierre 2023. Attention-based wildland fire spread modeling using fire-tracking satellite observations. Fire 6 (8) , 289. 10.3390/fire6080289

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Abstract

Modeling the spread of wildland fires is essential for assessing and managing fire risks. However, this task remains challenging due to the partially stochastic nature of fire behavior and the limited availability of observational data with high spatial and temporal resolutions. Herein, we propose an attention-based deep learning modeling approach that can be used to learn the complex behaviors of wildfires across different fire-prone regions. We integrate optimized spatial and channel attention modules with a convolutional neural network (CNN) modeling architecture and train the attention-based fire spread models using a recently derived fire-tracking satellite observational dataset in conjunction with corresponding fuel, terrain, and weather conditions. The evaluation results and their comparison with benchmark models, such as a deeper and more complex autoencoder model and the semi-empirical FARSITE fire behavior model, demonstrate the effectiveness of the attention-based models. These new data-driven fire spread models exhibit promising modeling performances in both the next-step prediction (i.e., predicting fire progression from one timestep earlier) and recursive prediction (i.e., recursively predicting final fire perimeters from initial ignition points) of observed large wildfires in California, and they provide a foundation for further practical applications including short-term active fire spread prediction and long-term fire risk assessment.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Earth and Environmental Sciences
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	MDPI
Date of First Compliant Deposit:	9 August 2023
Date of Acceptance:	26 July 2023
Last Modified:	10 Aug 2023 03:06
URI:	https://orca.cardiff.ac.uk/id/eprint/161548

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