Physically aligned forest fire risk prediction: A deep learning framework coupling fuel and climate multivariate factors.

Chen B, Zeng A, Xie Y, Wang X, Zhu L, Xie Q, Long G

DOI: 10.1371/journal.pone.0355829
Published: 2026
Container: PloS one
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pone.0355829,
  title = {Physically aligned forest fire risk prediction: A deep learning framework coupling fuel and climate multivariate factors.},
  author = {Chen B and Zeng A and Xie Y and Wang X and Zhu L and Xie Q and Long G},
  year = {2026},
  journal = {PloS one},
  doi = {10.1371/journal.pone.0355829},
  url = {https://doi.org/10.1371/journal.pone.0355829}
}

RIS

TY  - JOUR
TI  - Physically aligned forest fire risk prediction: A deep learning framework coupling fuel and climate multivariate factors.
AU  - Chen B
AU  - Zeng A
AU  - Xie Y
AU  - Wang X
AU  - Zhu L
AU  - Xie Q
AU  - Long G
PY  - 2026
JO  - PloS one
DO  - 10.1371/journal.pone.0355829
UR  - https://doi.org/10.1371/journal.pone.0355829
ER  -

APA

B, C., A, Z., Y, X., X, W., L, Z., Q, X., & G, L. (2026). Physically aligned forest fire risk prediction: A deep learning framework coupling fuel and climate multivariate factors.. PloS one. https://doi.org/10.1371/journal.pone.0355829

Source records

pubmed · retrieved 2026-09-25T18:06:03.605Z