Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction.

Casallas A, Jiménez-Saenz C, Torres V, Quirama-Aguilar M, Lizcano A, Lopez-Barrera EA, Ferro C, Celis N, Arenas R

Open source

DOI
10.3390/s22228790
Published
2022 Nov 14
Container
Sensors (Basel, Switzerland)
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.3390/s22228790,
  title = {Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction.},
  author = {Casallas A and Jiménez-Saenz C and Torres V and Quirama-Aguilar M and Lizcano A and Lopez-Barrera EA and Ferro C and Celis N and Arenas R},
  year = {2022},
  journal = {Sensors (Basel, Switzerland)},
  doi = {10.3390/s22228790},
  url = {https://doi.org/10.3390/s22228790}
}

RIS

TY  - JOUR
TI  - Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction.
AU  - Casallas A
AU  - Jiménez-Saenz C
AU  - Torres V
AU  - Quirama-Aguilar M
AU  - Lizcano A
AU  - Lopez-Barrera EA
AU  - Ferro C
AU  - Celis N
AU  - Arenas R
PY  - 2022
JO  - Sensors (Basel, Switzerland)
DO  - 10.3390/s22228790
UR  - https://doi.org/10.3390/s22228790
ER  -

APA

A, C., C, J., V, T., M, Q., A, L., EA, L., C, F., N, C., & R, A. (2022). Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s22228790

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