Machine learning applied to the oxygen-18 isotopic composition, salinity and temperature/potential temperature in the Mediterranean sea
UNIVERSAL IDENTIFIER: http://hdl.handle.net/11093/2887
EDITED VERSION: https://www.mdpi.com/2227-7390/9/19/2523
DOCUMENT TYPE: article
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.
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