Articles | Volume 23, issue 5
https://doi.org/10.5194/nhess-23-1755-2023
https://doi.org/10.5194/nhess-23-1755-2023
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

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Cited articles

acse-cz421: DL-WG/Digital-twin-LA-global-wildfire: Reduced-order digital twin and latent data assimilation for global wildfire prediction (v1.1.1), Zenodo [data set] and [code], https://doi.org/10.5281/zenodo.7866704, 2023. 
Amendola, M., Arcucci, R., Mottet, L., Casas, Q. C., Fan, S., Pain, C., Linden, P., and Guo, Y.: Data Assimilation in the Latent Space of a Convolutional Autoencoder, ICCS 2021, Lect. Notes Comput. Sc., 12746, 373–386, https://doi.org/10.1007/978-3-030-77977-1_30, 2021. 
Bauer, P., Stevens, B., and Hazeleger, W.: A digital twin of Earth for the green transition, Nat. Clim. Change, 11, 80–83, https://doi.org/10.1038/s41558-021-00986-y, 2021. 
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. 
Bianchi, F. M., De Santis, E., Rizzi, A., and Sadeghian, A.: Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition, IEEE, 3, 1931–1943, https://doi.org/10.1109/ACCESS.2015.2485943, 2015. 
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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.
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