IMIS | Flanders Marine Institute
 

Flanders Marine Institute

Platform for marine research

IMIS

Publications | Institutes | Persons | Datasets | Projects | Maps
[ report an error in this record ] Print this page

Neural network modelling of Baltic zooplankton abundances
Citable as data publication
Barth, A.; Herman, P.M.J.; (2018): Neural network modelling of Baltic zooplankton abundances. Marine Data Archive. https://doi.org/10.14284/381
Contact:

Availability: Creative Commons License This dataset is licensed under a Creative Commons Attribution 4.0 International License.

Description
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
Keywords:
Marine, Zooplankton, ANE, Baltic

Geographical coverage
ANE, Baltic [Marine Regions]

Temporal coverage
2007 - 2013

Contributors
Deltares, moredata creator
Université de Liège (ULG), moredata creator

Related datasets
Source datasets:
Finnish Baltic Sea zooplankton monitoring, more
ICES Zooplankton Community dataset, more
SHARK - Marine zooplankton monitoring in Sweden since 1979, more

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

Publication
Used in this dataset
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. http://hdl.handle.net/10.1175/JTECH-D-13-00130.1, more
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, more
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, more
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 . , more


Dataset status: Completed
Data type: Data products
Metadatarecord created: 2019-05-10
Information last updated: 2019-05-10
All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy