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Article

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

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Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, España
2
Universidade de Vigo, Departamento de Bioloxia Vexetal e Ciencias do Solo, 36310 Vigo, España
3
Universidade de Vigo, Departamento de Informática, Escola Superior Enxeñaría Informática, 32004 Ourense, España
*
Author to whom correspondence should be addressed.
Academic Editors: Theodore E. Simos and Charampos Tsitouras
Mathematics 2021, 9(19), 2523; https://doi.org/10.3390/math9192523
Received: 31 August 2021 / Revised: 26 September 2021 / Accepted: 28 September 2021 / Published: 8 October 2021
(This article belongs to the Special Issue Numerical Analysis and Scientific Computing)
This study proposed different techniques to estimate the isotope composition (δ18O), 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. View Full-Text
Keywords: machine learning; artificial neural network; random forest; support vector machine; oxygen isotopic composition; salinity; temperature; potential temperature; modelling machine learning; artificial neural network; random forest; support vector machine; oxygen isotopic composition; salinity; temperature; potential temperature; modelling
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MDPI and ACS Style

Astray, G.; Soto, B.; Barreiro, E.; Gálvez, J.F.; Mejuto, J.C. Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea. Mathematics 2021, 9, 2523. https://doi.org/10.3390/math9192523

AMA Style

Astray G, Soto B, Barreiro E, Gálvez JF, Mejuto JC. Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea. Mathematics. 2021; 9(19):2523. https://doi.org/10.3390/math9192523

Chicago/Turabian Style

Astray, Gonzalo, Benedicto Soto, Enrique Barreiro, Juan F. Gálvez, and Juan C. Mejuto. 2021. "Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea" Mathematics 9, no. 19: 2523. https://doi.org/10.3390/math9192523

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