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1
Estuary salinity prediction using a Support Vector Machine ..:
, In:
2023 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea)
,
Saccotelli, Leonardo
;
Verri, Giorgia
;
De Lorenzis, Alessandro
... - p. 294-298 , 2023
Link:
https://doi.org/10.1109/MetroSea58055.2023.10317103
RT T1 <cTitle>2023 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea)</cTitle>: T1 <Title>Estuary salinity prediction using a Support Vector Machine based approach: a case study of the Po di Goro estuary</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10317103&Exemplar=1&LAN=DE A1 Saccotelli, Leonardo A1 Verri, Giorgia A1 De Lorenzis, Alessandro A1 Caccioppoli, Rocco A1 Cherubini, Carla A1 Dimauro, Giovanni A1 Coppini, Giovanni A1 Maglietta, Rosalia YR 2023 K1 Support vector machines K1 Salinity (geophysical) K1 Biological system modeling K1 Sea measurements K1 Machine learning K1 Predictive models K1 Climate change K1 Estuaries K1 Support Vector Machine K1 Estuary Box Model K1 Estuary salinization SP 294 OP 298 LK http://dx.doi.org/https://doi.org/10.1109/MetroSea58055.2023.10317103 DO https://doi.org/10.1109/MetroSea58055.2023.10317103 SF ELIB - SuUB Bremen
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