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)

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_N...

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Bibliographic Details
Main Authors: Broullón, Daniel, Pérez, Fiz F., Doval, M. Dolores
Other Authors: Ministerio de Economía y Competitividad (España)
Format: Dataset
Language:English
Published: 2020
Subjects:
Total alkalinity
pH
Time series
Neural networks
Ocean acidification
Seasonal cycle
Long-term trends
Online Access:http://hdl.handle.net/10261/220930
https://doi.org/10.20350/digitalCSIC/12642
https://doi.org/10.13039/501100003329
Description
Summary: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 This research was supported by Ministerio de Educación, Cultura y Deporte (FPU grant FPU15/06026) and Ministerio de Economía y Competitividad through the ARIOS (CTM2016-76146-C3-1-R) project co-funded by the Fondo Europeo de Desarrollo Regional 2014-2020 (FEDER) No

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