The University of Southampton
University of Southampton Institutional Repository

Advancing process-based sargassum forecasts with earth observation and machine learning

Advancing process-based sargassum forecasts with earth observation and machine learning
Advancing process-based sargassum forecasts with earth observation and machine learning
Greene, Khalil
a3e1aa1b-ff2e-4125-a969-5aa616781932
Greene, Khalil
a3e1aa1b-ff2e-4125-a969-5aa616781932

Greene, Khalil (2025) Advancing process-based sargassum forecasts with earth observation and machine learning. SMMI PGR and Leverhulme Student Poster Event, University of Southampton, Southampton, United Kingdom. 14 Jan 2025. 1 pp .

Record type: Conference or Workshop Item (Poster)
Text
Khalil Greene - Author's Original
Download (2MB)

More information

Published date: 14 January 2025
Venue - Dates: SMMI PGR and Leverhulme Student Poster Event, University of Southampton, Southampton, United Kingdom, 2025-01-14 - 2025-01-14

Identifiers

Local EPrints ID: 498403
URI: http://eprints.soton.ac.uk/id/eprint/498403
PURE UUID: ec10b376-a500-42e7-a30d-dc4aaa51bb0c
ORCID for Khalil Greene: ORCID iD orcid.org/0009-0008-0115-6812

Catalogue record

Date deposited: 18 Feb 2025 17:32
Last modified: 22 Aug 2025 02:44

Export record

Contributors

Author: Khalil Greene ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×