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| Título: | Weekly reconstruction of pH and total alkalinity in an upwelling-dominated coastal ecosystem: The case of Ría de Vigo (NW Spain) between 1992 and 2019 (Discussions version) |
Autor: | Broullón, Daniel CSIC ORCID; Pérez, Fiz F. CSIC ORCID ; Doval, M. Dolores CSIC ORCID | Palabras clave: | Total alkalinity pH Time series Neural networks Ocean acidification Seasonal cycles Long-term trends |
Tesauro AGROVOC: | alkalinity neural networks ocean acidification |
Fecha de publicación: | 2020 | Editor: | DIGITAL.CSIC | Citación: | Broullón, Daniel; Pérez, Fiz F.; Doval, M. Dolores; 2020; "Weekly reconstruction of pH and total alkalinity in an upwelling-dominated coastal ecosystem: The case of Ría de Vigo (NW Spain) between 1992 and 2019 (Discussions version) [Dataset]"; Digital.Csic; http://dx.doi.org/10.20350/digitalCSIC/12642 | Descripción: | The item is made of 6 files: 1) README.txt; 2) INTECMAR_NN-database.csv: Dataset containing all the input variables used compute the time series of AT and pH as well as these two computed variables; 3) Training_database.xlsx: Dataset containing the data to train and test the neural networks; 4) pH_NN.mat is the neural network object used to compute the pH time series; 5) AT_NN.mat is the neural network object used to compute the total alkalinity time series; 6) Source_code.rar contains the MATLAB files to configure, train and validate the neural networks created in this study | URI: | http://hdl.handle.net/10261/220930 | DOI: | 10.20350/digitalCSIC/12642 |
| Aparece en las colecciones: | (IIM) Conjuntos de datos |
Ficheros en este ítem:
| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| INTECMAR_NN-database.csv | 5,54 MB | Unknown | Visualizar/Abrir | |
| AT_NN.mat | 13,27 kB | Unknown | Visualizar/Abrir | |
| pH_NN.mat | 8,06 kB | Unknown | Visualizar/Abrir | |
| Training_database.xlsx | 5,93 MB | Microsoft Excel XML | Visualizar/Abrir | |
| Source_code.rar | 10 kB | Unknown | Visualizar/Abrir | |
| README.txt | 687 B | Text | Visualizar/Abrir |
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