Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea
Abstract
:1. Introduction
- Artificial neural networks are a computational method inspired on the cell of the nervous system (known as neuron) [19] to try to analyse and reproduce the learning mechanism that owned by the more highly evolved animal species [20]. These models can find the relationships between inputs and outputs variables [21]. When the relationships are complex and highly non-linear, this kind of model needs a relatively huge training data group [22]. The ANNs are used as an option to statistical methods for different purposes such as estimation, classification, among others [23]. ANN approaches are popular due to their flexibility to fit random data and their reasonably uncomplicated development [23,24]. As previously stated, ANN models developed in this research are based on an MLP neural network, a popular ANN architecture [25]. ANNs are applied in different fields such as chemistry [26], medicine [27], food authenticity [28], among others [29,30]. This type of model can be part of more complex systems such as a smart healthcare monitoring system to predict heart disease that used ensemble deep learning [31] or to classify skin disease through deep learning neural networks stand on MobileNet V2 and long short-term memory [32].
- The second kind of model used is a random forest model. RF is a computational method for regression and/or classification [36] proposed by Breiman (2001) [36,37]. A random forest model is formed by decision trees where each tree utilizes a sample subset of available data [38], and the random forest’s prediction value is the average of all predicted values [38,39]. Random forest is one of the most capable machine learning approaches for forecasting [40] and can be used in different fields such as environmental science [38] and chemistry [41], among others [42,43].
- Finally, the last model developed is a support vector machine. An SVM model is a method enunciated by Boser et al. in 1992 [47,48]. Originally, the SVM models were developed for pattern recognition, nevertheless, nowadays they can be used to solve nonlinear regression problems or time series prediction [49,50] and due to its mathematical simplicity it has received much attention lately [51]. An SVM model creates a hyperplane, or hyperplanes, in a high- or infinite- dimensional space [52]. The hyperplane separates the dataset into a number of classes consistently with the training examples [53]. The principal advantage of SVM (compared to other classification techniques such as partial least square discriminant analysis) is its flexibility to model non-linear classification problems [54]. SVM models can be used in different areas such as: Engineering [55,56], Medicine [57,58], among others [59,60]. Related to this research field, SVM models can be used to estimate the SST in the tropical Atlantic [61] or to forecast the tropical Pacific SST anomalies [62]. In this case, Aguilar-Martinez used support vector regression and was compared with Bayesian neural network and linear regression models.
2. Materials and Methods
2.1. Database Used
2.2. Experimental Design
2.3. Methodologies
2.4. Fitting of Data and Modelling
2.5. Computational Resources
3. Results and Discussion
3.1. δ18 O Model
3.2. Salinity Model
3.3. Temperature/Potential Temperature Model
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Pierre et al. (1986) | Pierre (1999) | Gat et al. (1996) | Stahl and Rinow (1973) | |
---|---|---|---|---|
Maximum depth (m) | 4119 | 4103 | 2800 | 0 |
Minimum depth (m) | 2 | 1 | 0 | 0 |
Maximum temperature/potential temperature (°C) | 25.17 | 16.73 | 28.09 | 15.30 |
Minimum temperature/potential temperature (°C) | 12.76 | 12.38 | 13.38 | 14.50 |
Maximum salinity (‰) | 39.56 | 39.02 | 39.25 | 38.61 |
Minimum salinity (‰) | 37.29 | 36.39 | 38.38 | 38.48 |
Maximum δ18O (‰) | 1.89 | 1.68 | 2.42 | 1.74 |
Minimum δ18O (‰) | 1.21 | 0.70 | 1.13 | 1.58 |
Total samplings used | 92 | 267 | 109 | 2 |
δ18O Models | |||||||||
Model | r2T | RMSET | MAPET | r2V | RMSEV | MAPEV | r2Q | RMSEQ | MAPEQ |
ANN1 | 0.562 | 0.158 | 7.13 | 0.614 | 0.118 | 6.07 | 0.574 | 0.128 | 6.82 |
ANN2 | 0.607 | 0.150 | 6.61 | 0.641 | 0.115 | 5.90 | 0.654 | 0.119 | 6.19 |
RF | 0.889 | 0.084 | 3.84 | 0.682 | 0.107 | 5.01 | 0.739 | 0.098 | 4.98 |
SVM | 0.554 | 0.167 | 7.12 | 0.520 | 0.132 | 6.74 | 0.454 | 0.142 | 7.38 |
Salinity Models | |||||||||
Model | r2T | RMSET | MAPET | r2V | RMSEV | MAPEV | r2Q | RMSEQ | MAPEQ |
ANN1 | 0.891 | 0.167 | 0.27 | 0.877 | 0.170 | 0.25 | 0.931 | 0.154 | 0.23 |
ANN2 | 0.961 | 0.103 | 0.17 | 0.870 | 0.172 | 0.26 | 0.864 | 0.209 | 0.29 |
RF | 0.978 | 0.078 | 0.12 | 0.914 | 0.143 | 0.20 | 0.942 | 0.138 | 0.19 |
SVM | 0.899 | 0.159 | 0.22 | 0.884 | 0.165 | 0.29 | 0.913 | 0.166 | 0.27 |
Temperature/Potential Temperature Models | |||||||||
Model | r2T | RMSET | MAPET | r2V | RMSEV | MAPEV | r2Q | RMSEQ | MAPEQ |
ANN1 | 0.937 | 0.745 | 3.95 | 0.931 | 0.757 | 3.95 | 0.923 | 0.699 | 3.99 |
ANN2 | 0.934 | 0.717 | 3.07 | 0.951 | 0.621 | 3.29 | 0.894 | 0.777 | 3.34 |
RF | 0.972 | 0.467 | 1.99 | 0.972 | 0.452 | 2.26 | 0.953 | 0.513 | 2.44 |
SVM | 0.942 | 0.676 | 1.86 | 0.926 | 0.722 | 3.00 | 0.949 | 0.516 | 2.54 |
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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
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 StyleAstray, 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
APA StyleAstray, G., Soto, B., Barreiro, E., Gálvez, J. F., & Mejuto, J. C. (2021). Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea. Mathematics, 9(19), 2523. https://doi.org/10.3390/math9192523