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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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.....
JournalFrontiers in Marine Science
Sustainable Development Goals (SDG)14.A
Essential Ocean Variables (EOV)Nutrients
Maturity LevelTRL 8 Actual system completed and "mission qualified" through test and demonstration in an operational environment (ground or space)
Best Practice TypeBest Practice
Manual (incl. handbook, guide, cookbook etc)
Spatial CoverageMediterranean Sea
CitationFourrier, 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.2020.00620
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