Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior-Reference-Cited by-同舟云学术

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Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior

Published:2023-08-07 Issue:6 Volume:59 Page:3327-3354
ISSN:0015-2684
Container-title:Fire Technology
language:en
Short-container-title:Fire Technol

Author:

Burge John^ORCID,Bonanni Matthew R.^ORCID,Hu R. Lily,Ihme Matthias^ORCID

Funder

NASA Fellowship Activity

Stanford University School of Engineering Departmental Fellowship

Publisher

Springer Science and Business Media LLC

Subject

Safety, Risk, Reliability and Quality,General Materials Science,Building and Construction

Link

https://link.springer.com/content/pdf/10.1007/s10694-023-01469-6.pdf

Reference37 articles.

1. Covington WW, Moore MM (1994) Southwestern ponderosa forest structure: changes since Euro-American settlement. J Forest 92:39–47

2. Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increase Western U.S. forest wildfire activity. Science 313:940–943

3. Mueller SE, Thode AE, Margolis EQ, Yocom LL, Young JD, Iniguez JM (2020) Climate relationships with increasing wildfire in the southwestern US from 1984 to 2015. For Ecol Manage 460:117861

4. Thomas D, Butry D, Gilbert S, Webb D, Fung J (2017) The costs and losses of wildfires: a literature survey. NIST Special Publication 1215. National Institute of Standards and Technology, Gaithersburg

5. Kollanus V, Prank M, Gens A, Soares J, Vira J, Kukkonen J, Sofiev M, Salonen RO, Lanki T (2017) Mortality due to vegetation fire-originated PM2.5 exposure in Europe-Assessment for the years 2005 and 2008. Environ Health Perspect 125:30–37

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