Reduced-order digital twin and latent data assimilation for global wildfire prediction

Zhong, Caili; Cheng, Sibo; Kasoar, Matthew; Arcucci, Rossella

doi:https://doi.org/10.5194/nhess-23-1755-2023

Articles | Volume 23, issue 5

https://doi.org/10.5194/nhess-23-1755-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Advances in machine learning for natural hazards risk...

https://doi.org/10.5194/nhess-23-1755-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 5

Research article

|

12 May 2023

Research article |

| 12 May 2023

Reduced-order digital twin and latent data assimilation for global wildfire prediction

Caili Zhong, Sibo Cheng, Matthew Kasoar, and Rossella Arcucci

Data sets

DL-WG/Digital-twin-LA-global-wildfire: Reduced-order digital twin and latent data assimilation for global wildfire prediction (v1.1.1) acse-cz421 https://doi.org/10.5281/zenodo.7866704

Model code and software

DL-WG/Digital-twin-LA-global-wildfire: Reduced-order digital twin and latent data assimilation for global wildfire prediction (v1.1.1) acse-cz421 https://doi.org/10.5281/zenodo.7866704

Short summary

This paper introduces a digital twin fire model using machine learning techniques to improve the efficiency of global wildfire predictions. The proposed model also manages to efficiently adjust the prediction results thanks to data assimilation techniques. The proposed digital twin runs 500 times faster than the current state-of-the-art physics-based model.