K nearest neighbors classification of water masses in the western Alboran Sea using the sigma-pi diagram
Type | Article | ||||||||||||
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Date | 2023-06 | ||||||||||||
Language | English | ||||||||||||
Author(s) | Belattmania Ayoub1, El Arrim Abdelkrim1, Ayouche Adam2, Charria Guillaume5, Hilmi Karim3, El Moumni Bouchta4 | ||||||||||||
Affiliation(s) | 1 : Faculté des Sciences et Techniques, Tanger, Morocco 2 : Laboratory for Ocean Physics and Satellite Remote Sensing (LOPS), UMR6523, Ifremer, Univ. Brest, CNRS, IRD, Brest, France 3 : Institut National de Recherche Halieutique, Casablanca, Morocco 4 : Université Abdelmalek Essaâdi, Tétouan, Morocco 5 : Laboratory for Ocean Physics and Satellite Remote Sensing (LOPS), UMR6523, Ifremer, Univ. Brest, CNRS, IRD, Brest, France |
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Source | Deep-sea Research Part I-oceanographic Research Papers (0967-0637) (Elsevier BV), 2023-06 , Vol. 196 , P. 104024 (26p.) | ||||||||||||
DOI | 10.1016/j.dsr.2023.104024 | ||||||||||||
Keyword(s) | Alboran sea, Western Alboran Gyre, Water masses, (& sigma, -& pi, ) diagram, K nearest neighbor classification | ||||||||||||
Abstract | Different classification techniques of water masses have been developped using the potential temperature-salinity (θ-S) diagram and its volumetric analysis. In this study, we propose a new method to automatically classify water masses via a supervised machine learning algorithm based on the K nearest neighbors (Knn), in the potential density and potential spicity (σ-π) coordinates. This method is applied to temperature and salinity data collected in the western side of the Alboran Sea during a glider mission, dedicated to sample the Western Alboran Gyre (WAG) in late winter 2021. The water masses in the studied region were classified into five different categories following a supervised learning process, based on ocean profile databases available on the region of interest. The results corroborate previous studies of the spatial distribution of water masses in the Alboran Sea, inferred from traditional method based on the expert analysis of the (θ-S) diagram, and suggest that this methodology is efficient and reliable for water masses classification. Compared to a classical clustering computation (herein k-means), this method is more appropriate in a region where the characteristics of the water masses change considerably in both space and time. |
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