Journal of
Oceanography and Marine Science

  • Abbreviation: J. Oceanogr. Mar. Sci.
  • Language: English
  • ISSN: 2141-2294
  • DOI: 10.5897/JOMS
  • Start Year: 2010
  • Published Articles: 60

Article in Press

Applications of Support vector machine learning in Oceanography

Hafez Ahmad

  •  Received: 22 March 2019
  •  Accepted: 06 August 2019
Support vector machine is a supervised machine learning (ML) algorithm which splits cases of different class labels. SVM supports both regression and classification purposes and can handle multiple continuous and categorical variables. SVM Algorithm has proven to be a powerful tool for analysing ocean data, recognizing patter oceanographic phenomenon with high accuracy in an efficient way. ML has a wide spectrum of real time applications in oceanography and earth sciences. This study has explained in simple way the realistic uses and applications of support vector machine (SVM) ML algorithms. Major applications of SVM in Oceanography are the prediction of ocean weather and climate, habitat modelling and distribution, marine resources management, detection of the oil spill and pollution and wave modelling. This review focuses on SVM introduction in simple way, recent advances, applications of image(satellite images) classification, pattern recognition of oceanic aspects, ocean colour study and water quality monitoring using SVM and ocean events ,classification of marine species and parameters (SST, wave , current ,wave) prediction and forecasting of linear or nonlinear data.

Keywords: Support vector machine, Ocean data, Prediction, Application