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Neural network modelling of Baltic zooplankton abundances
Citeerbaar als data publicatie
Barth, A.; Herman, P.M.J.; (2018): Neural network modelling of Baltic zooplankton abundances. Marine Data Archive.

Beschikbaarheid: Creative Commons License Deze dataset valt onder een Creative Commons Naamsvermelding 4.0 Internationaal-licentie.

This data product is a series of gridded abundance maps for 40 zooplankton species from 2007 to 2013 in the Baltic Sea, based on a neural network analysis. As input data a combination of EMODnet Biology datasets were used, together with the environmental variables dissolved oxygen, salinity, temperature, chlorophyll concentration bathymetry and the distance from coast. Additionally the position (latitude and longitude) and the year are provided to the neural network. DIVAnd (n-dimensional Data-Interpolating Variational Analysis) and the neural network library Knet were used in this analysis.

Biologie > Plankton > Zooplankton
Marien/Kust, Zooplankton, ANE, Baltic

Geografische spreiding
ANE, Baltic [Marine Regions]

Spreiding in de tijd
2007 - 2013

Bijdrage door
Université de Liège (ULG), meerdata creator
Deltares, meerdata creator

Gerelateerde datasets
Bron datasets:
Finnish Baltic Sea zooplankton monitoring, meer
ICES Zooplankton Community dataset, meer
SHARK - National marine environmental monitoring of zooplankton in Sweden since 1979, meer

EMODNETBIO III: European Marine Observation and Data Network- Biology III, meer

Gebruikt in deze dataset
Yuret, D. (2016). Knet: beginning deep learning with 100 lines of Julia, in: NIPS 2016: Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, December 5-10, 2016 . , meer
Barth, A. et al. (2014). divand-1.0: n-dimensional variational data analysis for ocean observations. Geosci. Model Dev. 7(1): 225-241., meer
Beckers, J.-M. et al. (2014). Approximate and efficient methods to assess error fields in spatial gridding with Data Interpolating Variational Analysis (DIVA). J. Atmos. Oceanic. Technol. 31(2): 515-530., meer
Troupin, C. et al. (2012). Generation of analysis and consistent error fields using the Data Interpolating Variational Analysis (DIVA). Ocean Modelling 52-53: 90-101., meer

Dataset status: Afgelopen
Data type: Dataproducten
Metadatarecord aangemaakt: 2019-05-10
Informatie laatst gewijzigd: 2022-06-02
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