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

Gonzalo Astray, Benedicto Soto, Enrique Barreiro, Juan F. Gálvez and Juan C. Mejuto
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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

Mathematics, 2021, vol. 9, issue 19, 1-15

Abstract: This study proposed different techniques to estimate the isotope composition (? 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.

Keywords: machine learning; artificial neural network; random forest; support vector machine; oxygen isotopic composition; salinity; temperature; potential temperature; modelling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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