Forecasting of absolute dynamic topography using deep learning algorithm with application to the Baltic Sea
author
Rajabi-Kiasari, Saeed
Delpeche-Ellmann, Nicole Camille
Ellmann, Artu
source
Computers & geosciences
publisher
Elsevier
journal volume number month
vol. 178
year of publication
2023
pages
art. 105406, 16 p. : ill
url
https://doi.org/10.1016/j.cageo.2023.105406
subject term
topograafia
geodeesia
veetase
merevesi
sügavõpe
geoid
subject of location
Läänemeri
keyword
dynamic topography
deep learning (DL)
Baltic Sea
sea-level prediction
hydro-geodesy
geoid
ISSN
0098-3004
notes
Includes bibliogr
scientific publication
teaduspublikatsioon
classifier
1.1
Scopus
https://www.scopus.com/sourceid/110327
https://www.scopus.com/record/display.uri?eid=2-s2.0-85164237790&origin=inward&txGid=ab1587637a95cc34718ff33707b7fd85
WOS
https://jcr.clarivate.com/jcr-jp/journal-profile?journal=COMPUT%20GEOSCI-UK&year=2023
https://www.webofscience.com/wos/woscc/full-record/WOS:001028982100001
category (general)
Earth and planetary sciences
Maa- ja planeediteadused
Computer science
Arvutiteadus
category (sub)
Earth and planetary sciences. Computers in earth sciences
Maa- ja planeediteadused. Arvutid maateadustes
Computer science. Information systems
Arvutiteadus. Infosüsteemid
quartile
Q1
TalTech department
ehituse ja arhitektuuri instituut
küberneetika instituut
language
inglise
Reserch Group
Wave engineering research group
Road engineering and geodesy research group