Classwms: A MATLAB GUI for water masses classification using clustering analysis and K Nearest Neighbors

In the realm of oceanography, understanding the complex dynamics of Earth's oceans is crucial. One key aspect of this understanding is the classification of water masses based on their physical properties, such as temperature and salinity. Clustering analysis has emerged as a powerful method fo...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:SoftwareX 2024-05, Vol.26, p.101741, Article 101741
Hauptverfasser: Belattmania, Ayoub, Hakkou, Mounir, Chtioui, Taoufiq, Aangri, Abdelhaq, Abdenour, Assia, Latni, El mehdi, Benharra, Abdessalam, Arrim, Abdelkrim EL, Dahaoui, Azdine, Raissouni, Ahmed, Khali Issa, Lamiae, ED-daoudy, Lhoussaine
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In the realm of oceanography, understanding the complex dynamics of Earth's oceans is crucial. One key aspect of this understanding is the classification of water masses based on their physical properties, such as temperature and salinity. Clustering analysis has emerged as a powerful method for automated water masses classification using the temperature-salinity (T-S) diagram. However, it faces limitations in regions with complex mixing processes. To address this, a novel approach using the K Nearest Neighbors (KNN) algorithm based on potential density and potential spicity (σ-π) diagrams was proposed. In this context, we introduce the classwms tool, a user-friendly MATLAB Graphical User Interface (GUI) that combines clustering analysis and KNN classification for efficient water mass classification.
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2024.101741