Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting

Author:

Cheng SiboORCID,Prentice I. Colin,Huang YuhanORCID,Jin Yufang,Guo Yi-Ke,Arcucci Rossella

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modeling and Simulation,Numerical Analysis

Reference85 articles.

1. NICC wildland fire summary and statistics annual report, 2021.

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3. Assessing the suitability of soft computing approaches for forest fires prediction;Al_Janabi;Appl. Comput. Inf.,2018

4. M. Amendola, R. Arcucci, L. Mottet, C.Q. Casas, S. Fan, C. Pain, P. Linden, Y.-K. Guo, Data assimilation in the latent space of a neural network, 2020.

5. Current status and future needs of the behaveplus fire modeling system;Andrews;Int. J. Wildland Fire,2013

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