n ear f uture p elagic c atch p lanning
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N EAR F UTURE P ELAGIC C ATCH P LANNING Brd Johan Hanssen - SINTEF - PowerPoint PPT Presentation

This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 732064 This project is part of BDV PPP T EAM CLP CLP- D ECISION S UPPORT S YSTEM FOR N EAR F UTURE P ELAGIC C


  1. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732064 This project is part of BDV PPP T EAM CLP CLP- D ECISION S UPPORT S YSTEM FOR N EAR F UTURE P ELAGIC C ATCH P LANNING Bård Johan Hanssen - SINTEF Nord Peter Halland Haro - SINTEF Digital EMODnet OpenSeaLab Hackathon Per Gunnar Auran - SINTEF Ocean Antwerp, 17 th November 2017 Pekka Kotilainen - SYKE MRC Ahmet Bilici - PROMATECH This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme 1 under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.

  2. Project Context - DataBio WP3 – Fishery pilots + other related projects in our portifolio Optimization of … Essential value chain driver: Where to fish what when? … through utilization of Big Data Technology (48 international partners) This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme 2 under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.

  3. TEAM CLP – What & how? Where is the fish likely to be? Physical Modeling – long term p(x,y,z,t | species) Machine Learning – Now (demo) (SINTEF: 1970s -2010s) Knowledge transfer synergy: - Feature strength & importance) - Detailed data for f(x,y,z,t) - training Deep learning NN of catch probability : - High catch probability heatmap - Black box model trained on - catch data - physics, chemistry ++ This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme 3 under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.

  4. TEAM CLP – Why? – Most rele levant busin iness vie iew from C2 pilo ilot Stakeholders Business goals & supporting processes Big Data analytics Catch & Market data This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme 4 under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.

  5. Thank you for your attention! Team CLP & Partners in SINTEF & DataBio Contact persons : - {peter.haro|bard.hanssen|per.gunnar.auran}@sintef.no This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme 5 under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.

  6. DataBio WP3 – Fishery pilots – big picture elements This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme 6 under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.

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