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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. https://doi.org/10.14284/381
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 Deze dataset valt onder een Creative Commons Naamsvermelding 4.0 Internationaal-licentie.Beschrijving
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. Scope Thema's: Biologie > Plankton > Zooplankton Kernwoorden: Marien/Kust, Biota, Data niet gecontroleerd, Geen beperkingen op publieke toegang, Geowetenschappelijke informatie, Metadata niet gecontroleerd, NetCDF (Network Common Data Form), Oceanen, Regionaal, Systemen voor verwijzing door middel van coördinaten, WGS84 (EPSG:4326), Zooplankton, ANE, Baltic Geografische spreiding ANE, Baltic [Marine Regions] Spreiding in de tijd
2007 - 2013 Gerelateerde datasets
Project
EMODNETBIO III: European Marine Observation and Data Network- Biology III, meer
Publicatie
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. https://dx.doi.org/10.5194/gmd-7-225-2014, 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. https://dx.doi.org/10.1175/JTECH-D-13-00130.1, 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. https://dx.doi.org/10.1016/j.ocemod.2012.05.002, meer
URL's
Dataset status: Afgelopen
Data type: Dataproducten
Metadatarecord aangemaakt: 2019-05-10
Informatie laatst gewijzigd: 2024-12-04
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