Wildland Fire Burned Areas Prediction Using Long Short-Term Memory Neural Network with Attention Mechanism-Reference-Cited by-同舟云学术

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Wildland Fire Burned Areas Prediction Using Long Short-Term Memory Neural Network with Attention Mechanism

Published:2020-08-12 Issue:6 Volume:57 Page:1-23
ISSN:0015-2684
Container-title:Fire Technology
language:en
Short-container-title:Fire Technol

Author:

Li Zhongzhi,Huang Yufeng^ORCID,Li Xiaoxue,Xu Lei

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Safety, Risk, Reliability and Quality,General Materials Science

Link

https://link.springer.com/content/pdf/10.1007/s10694-020-01028-3.pdf

Reference56 articles.

1. Neary DG, Ryan KC, DeBano LF (2005) Wildland fire in ecosystems: effects of fire on soils and water. https://doi.org/10.2737/rmrs-gtr-42-v4

2. Downing TA, Imo M, Kimanzi J, Otinga AN (2017) Effects of wildland fire on the tropical alpine moorlands of Mount Kenya. Catena 149:300–308. https://doi.org/10.1016/j.catena.2016.10.003

3. Storey MA, Price OF, Sharples JJ, Bradstock RA (2020) Drivers of long-distance spotting during wildfires in south-eastern Australia. Int J Wildland Fire. https://doi.org/10.1071/wf19124

4. Gibson R, Danaher T, Hehir W, Collins L (2020) A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest. Remote Sens Environ. https://doi.org/10.1016/j.rse.2020.111702

5. Saeedian P, Moran B, Tolhurst K, Malka NH (2010) Prediction of high-risk areas in wildland fires. Paper presented at the international conference on information and automation, 12 Jan 2010

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