| Autor: |
Ayoub Belattmania, Mounir Hakkou, Taoufiq Chtioui, Abdelhaq Aangri, Assia Abdenour, El mehdi Latni, Abdessalam Benharra, Abdelkrim EL Arrim, Azdine Dahaoui, Ahmed Raissouni, Lamiae Khali Issa, Lhoussaine ED-daoudy |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
SoftwareX, Vol 26, Iss , Pp 101741- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2352-7110 |
| DOI: |
10.1016/j.softx.2024.101741 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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