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Conference Papers Year : 2020

Artificial Intelligence, Machine Learning and Modeling for Understanding the Oceans and Climate Change

Nayat Sanchez-Pi
  • Function : Author
Luis Marti
André Abreu
  • Function : Author
Alejandro Maass
  • Function : Author
  • PersonId : 835545
Pablo A. Marquet
  • Function : Author
  • PersonId : 1019362
Julien Salomon
Marc Schoenauer
Michele Sebag
  • Function : Author
  • PersonId : 836537

Abstract

The ongoing transformation of climate and biodiversity will have a drastic impact on almost all forms of life in the ocean with further consequences on food security, ecosystem services in coastal and inland communities. Despite these impacts, scientific data and infrastructures are still lacking to understand and quantify the consequences of these perturbations on the marine ecosystem. Understanding this phenomenon is not only an urgent but also a scientifically demanding task. Consequently, it is a problem that must be addressed with a tific cohort approach, where multi-disciplinary teams collaborate to bring the best of different scientific areas. In this proposal paper, we describe our newly launched four-years project focusedon developing new artificial intelligence, machine learning, and mathematical modeling tools to contribute to the understanding of the structure, functioning, and underlying mechanisms and dynamics of the global ocean symbiome and its relation with climate change. These actions should enable the understanding of our oceans and predict and mitigate the consequences of climate and biodiversity changes.
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Dates and versions

hal-03138712 , version 1 (11-02-2021)

Identifiers

  • HAL Id : hal-03138712 , version 1

Cite

Nayat Sanchez-Pi, Luis Marti, André Abreu, Olivier Bernard, Colomban de Vargas, et al.. Artificial Intelligence, Machine Learning and Modeling for Understanding the Oceans and Climate Change. NeurIPS 2020 Workshop - Tackling Climate Change with Machine Learning, Dec 2020, Santiago / Virtual, Chile. ⟨hal-03138712⟩
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