Performance and Efficiency of Machine Learning Based Approaches for Wildfire Susceptibility Mapping

Details

Ressource 1Download: environsciproc-17-00038.pdf (550.94 [Ko])
State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_6DCBAA757913
Type
Inproceedings: an article in a conference proceedings.
Publication sub-type
Abstract (Abstract): shot summary in a article that contain essentials elements presented during a scientific conference, lecture or from a poster.
Collection
Publications
Institution
UNIL/CHUV
Title
Performance and Efficiency of Machine Learning Based Approaches for Wildfire Susceptibility Mapping
Title of the conference
ICFBR 2022
Author(s)
Tonini Marj, Pereira Mario G., Fiorucci Paolo
Publication state
Published
Issued date
09/08/2022
Volume
17
Pages
38
Language
english
Keywords
wildfires mapping, land cover, machine learning, model validation, GIS
URN
urn:nbn:ch:serval-BIB_6DCBAA7579133
OAI-PMH
oai:serval.unil.ch:BIB_6DCBAA757913
DOI
10.3390/environsciproc2022017038
Open Access
Yes
Create date
07/09/2022 15:18
Last modification date
11/01/2023 8:11
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