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Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea

Author

Listed:
  • Gonzalo Astray

    (Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, España)

  • Benedicto Soto

    (Universidade de Vigo, Departamento de Bioloxia Vexetal e Ciencias do Solo, 36310 Vigo, España)

  • Enrique Barreiro

    (Universidade de Vigo, Departamento de Informática, Escola Superior Enxeñaría Informática, 32004 Ourense, España)

  • Juan F. Gálvez

    (Universidade de Vigo, Departamento de Informática, Escola Superior Enxeñaría Informática, 32004 Ourense, España)

  • Juan C. Mejuto

    (Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, España)

Abstract

This study proposed different techniques to estimate the isotope composition (δ 18 O), salinity and temperature/potential temperature in the Mediterranean Sea using five different variables: (i–ii) geographic coordinates (Longitude, Latitude), (iii) year, (iv) month and (v) depth. Three kinds of models based on artificial neural network (ANN), random forest (RF) and support vector machine (SVM) were developed. According to the results, the random forest models presents the best prediction accuracy for the querying phase and can be used to predict the isotope composition (mean absolute percentage error (MAPE) around 4.98%), salinity (MAPE below 0.20%) and temperature (MAPE around 2.44%). These models could be useful for research works that require the use of past data for these variables.

Suggested Citation

  • Gonzalo Astray & Benedicto Soto & Enrique Barreiro & Juan F. Gálvez & Juan C. Mejuto, 2021. "Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea," Mathematics, MDPI, vol. 9(19), pages 1-15, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2523-:d:651480
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    References listed on IDEAS

    as
    1. Lins, Isis Didier & Araujo, Moacyr & Moura, Márcio das Chagas & Silva, Marcus André & Droguett, Enrique López, 2013. "Prediction of sea surface temperature in the tropical Atlantic by support vector machines," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 187-198.
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    4. Anonymous, 2013. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 8(2), pages 129-130, November.
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