Spatiotemporal data science: theoretical advances and applications

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Serval ID
serval:BIB_6DA06F073A9C
Type
Article: article from journal or magazin.
Publication sub-type
Editorial
Collection
Publications
Institution
UNIL/CHUV
Title
Spatiotemporal data science: theoretical advances and applications
Journal
Stochastic Environmental Research and Risk Assessment
Author(s)
Amato Federico, Lombardo Luigi, Tonini Marj, Marvuglia Antonino, Castro-Camilo Daniela, Guignard Fabian
ISSN
1436-3240
1436-3259
Publication state
Published
Issued date
08/2022
Volume
36
Number
8
Pages
2027-2029
Language
english
Keywords
General Environmental Science, Safety, Risk, Reliability and Quality, Water Science and Technology, Environmental Chemistry, Environmental Engineering
OAI-PMH
oai:serval.unil.ch:BIB_6DA06F073A9C
DOI
10.1007/s00477-022-02281-4
Web of science
000844931000001
Open Access
Yes
Create date
07/09/2022 14:09
Last modification date
11/01/2023 7:52
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