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

DOI10.1016/j.jcp.2022.111302OpenAlexW4280515676WikidataQ117803583 ScholiaQ117803583MaRDI QIDQ2672756

I. Colin Prentice, Rossella Arcucci, Yufang Jin, Sibo Cheng, Yuhan Huang, Yi Ke Guo

Publication date: 13 June 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2022.111302

zbMATH Keywords

data assimilationdeep learningreduced-order modellingfire spreadLSTMwildfire forecasting

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Thermodynamics and heat transfer (80Axx) Probabilistic methods, stochastic differential equations (65Cxx)

Related Items (3)

Efficient deep data assimilation with sparse observations and time-varying sensors ⋮ Hardware-friendly compression and hardware acceleration for transformer: a survey ⋮ Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models

Uses Software

• FARSITE
• FlamMap

Cites Work

• A wildland fire model with data assimilation
• Low-rank approximations for computing observation impact in 4D-Var data assimilation
• An artificial neural network framework for reduced order modeling of transient flows
• Radiant heat flux modelling for wildfires
• A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990
• 10.1162/15324430260185646
• Turbulence and the dynamics of coherent structures. I. Coherent structures
• Eigenvalues, invariant factors, highest weights, and Schubert calculus
• Data Assimilation
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

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