FN Archimer Export Format
PT J
TI K nearest neighbors classification of water masses in the western Alboran Sea using the sigma-pi diagram
BT 
AF Belattmania, Ayoub
   El Arrim, Abdelkrim
   Ayouche, Adam
   Charria, Guillaume
   Hilmi, Karim
   El Moumni, Bouchta
AS 1:1;2:1;3:2;4:5;5:3;6:4;
FF 1:;2:;3:;4:PDG-ODE-LOPS-OC;5:;6:;
C1 Faculté des Sciences et Techniques, Tanger, Morocco
   Laboratory for Ocean Physics and Satellite Remote Sensing (LOPS), UMR6523, Ifremer, Univ. Brest, CNRS, IRD, Brest, France
   Institut National de Recherche Halieutique, Casablanca, Morocco
   Université Abdelmalek Essaâdi, Tétouan, Morocco
   Laboratory for Ocean Physics and Satellite Remote Sensing (LOPS), UMR6523, Ifremer, Univ. Brest, CNRS, IRD, Brest, France
C2 UNIV TANGER, MOROCCO
   UBO, FRANCE
   INRH, MOROCCO
   UNIV ABDELMALEK ESSAADI, MOROCCO
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOPS-OC
UM LOPS
IN WOS Ifremer UMR
   WOS Cotutelle UMR
   copubli-france
   copubli-univ-france
   copubli-int-hors-europe
   copubli-sud
IF 2.4
TC 0
UR https://archimer.ifremer.fr/doc/00828/93980/100803.pdf
LA English
DT Article
DE ;Alboran sea;Western Alboran Gyre;Water masses;(& sigma;-& pi;) diagram;K nearest neighbor classification
AB 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. 
PY 2023
PD JUL
SO Deep-sea Research Part I-oceanographic Research Papers
SN 0967-0637
PU Elsevier BV
VL 196
UT 001042848200001
DI 10.1016/j.dsr.2023.104024
ID 93980
ER

EF