Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data
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arXiv
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| Format: | Preprint |
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2024
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| author | Pang, Bo Cheng, Sibo Huang, Yuhan Jin, Yufang Guo, Yike Prentice, I. Colin Harrison, Sandy P. Arcucci, Rossella |
| author_facet | Pang, Bo Cheng, Sibo Huang, Yuhan Jin, Yufang Guo, Yike Prentice, I. Colin Harrison, Sandy P. Arcucci, Rossella |
| contents | Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behaviour. Existing physics-based models are limited in predicting large or long-duration wildfire events. Here, we develop a deep-learning-based predictive model, Fire-Image-DenseNet (FIDN), that uses spatial features derived from both near real-time and reanalysis data on the environmental and meteorological drivers of wildfire. We trained and tested this model using more than 300 individual wildfires that occurred between 2012 and 2019 in the western US. In contrast to existing models, the performance of FIDN does not degrade with fire size or duration. Furthermore, it predicts final burnt area accurately even in very heterogeneous landscapes in terms of fuel density and flammability. The FIDN model showed higher accuracy, with a mean squared error (MSE) about 82% and 67% lower than those of the predictive models based on cellular automata (CA) and the minimum travel time (MTT) approaches, respectively. Its structural similarity index measure (SSIM) averages 97%, outperforming the CA and FlamMap MTT models by 6% and 2%, respectively. Additionally, FIDN is approximately three orders of magnitude faster than both CA and MTT models. The enhanced computational efficiency and accuracy advancements offer vital insights for strategic planning and resource allocation for firefighting operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01400 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data Pang, Bo Cheng, Sibo Huang, Yuhan Jin, Yufang Guo, Yike Prentice, I. Colin Harrison, Sandy P. Arcucci, Rossella Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behaviour. Existing physics-based models are limited in predicting large or long-duration wildfire events. Here, we develop a deep-learning-based predictive model, Fire-Image-DenseNet (FIDN), that uses spatial features derived from both near real-time and reanalysis data on the environmental and meteorological drivers of wildfire. We trained and tested this model using more than 300 individual wildfires that occurred between 2012 and 2019 in the western US. In contrast to existing models, the performance of FIDN does not degrade with fire size or duration. Furthermore, it predicts final burnt area accurately even in very heterogeneous landscapes in terms of fuel density and flammability. The FIDN model showed higher accuracy, with a mean squared error (MSE) about 82% and 67% lower than those of the predictive models based on cellular automata (CA) and the minimum travel time (MTT) approaches, respectively. Its structural similarity index measure (SSIM) averages 97%, outperforming the CA and FlamMap MTT models by 6% and 2%, respectively. Additionally, FIDN is approximately three orders of magnitude faster than both CA and MTT models. The enhanced computational efficiency and accuracy advancements offer vital insights for strategic planning and resource allocation for firefighting operations. |
| title | Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.01400 |