Spatiotemporal data science: theoretical advances and applications

Details

Serval ID
serval:BIB_6DA06F073A9C
Type
Article: article from journal or magazin.
Collection
Publications
Institution
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
26/08/2022
Language
english
Keywords
General Environmental Science, Safety, Risk, Reliability and Quality, Water Science and Technology, Environmental Chemistry, Environmental Engineering
Web of science
Open Access
Yes
Create date
07/09/2022 13:09
Last modification date
08/09/2022 5:41
Usage data