A Regional Neural Network Approach to Estimate Water-Column Nutrient Concentrations and Carbonate System Variables in the Mediterranean Sea: CANYON-MED.

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Date
2020Author
Fourrier, Marine
Coppola, Laurent
Claustre, Hervé
D’Ortenzio, Fabrizio
Sauzède, Raphaëlle
Gattuso, Jean-Pierre
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A regional neural network-based method, “CANYON-MED” is developed to estimate nutrients and carbonate system variables specifically in the Mediterranean Sea over the water column from pressure, temperature, salinity, and oxygen together with geolocation and date of sampling. Six neural network ensembles were developed, one for each variable (i.e., three macronutrients: nitrates (NO−3), phosphates (PO3−4) and silicates (SiOH4), and three carbonate system variables: pH on the total scale (pHT),total alkalinity (AT), and dissolved inorganic carbon or total carbon (CT), trained using a specific quality-controlled dataset of reference “bottle” data in the MediterraneanSea. This dataset is representative of the peculiar conditions of this semi-enclosed sea, as opposed to the global ocean. For each variable, the neural networks were trained on 80% of the data chosen randomly and validated using the remaining 20%.CANYON-MED retrie.....
Journal
Frontiers in Marine ScienceVolume
7Issue
Article 620Page Range
20pp.Document Language
enSustainable Development Goals (SDG)
14.AEssential Ocean Variables (EOV)
NutrientsInorganic carbon
Maturity Level
TRL 8 Actual system completed and "mission qualified" through test and demonstration in an operational environment (ground or space)Best Practice Type
Best PracticeManual (incl. handbook, guide, cookbook etc)
Spatial Coverage
Mediterranean SeaDOI Original
https://doi.org/10.3389/fmars.2020.00620Citation
Fourrier, M.; Coppola, L.; Claustre, H.; D’Ortenzi, F.; Sauzède, R. and Gattuso, J-.P (2020) A Regional Neural Network Approach to Estimate Water-Column Nutrient Concentrations and Carbonate System Variables in the Mediterranean Sea: CANYON-MED. Frontiers in Marine Science, 7: 620, 20pp. DOI: 10.3389/fmars.2020.00620Collections
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