Skip to main content

User account menu

  • Log in
Home

Navigation principale

  • Home
  • About us
    • CLAND Convergence Institute
    • CLAND Partners
    • CLAND Newcomers
  • Research
    • Our research
    • Challenge 1
    • Challenge 2
    • Challenge 3
    • Publications
  • Graduate Program
    • Our Graduate Program
    • Our Internship Program
    • 2021 Call for Master Grants
    • Internship Offers
  • News
    • 2021 Call for PhD topics
    • 2020 Call for PhD topics
  • Events
    • 2021
      • Nature-based solutions
    • 2020
      • Machine Learning
      • Albedo & Climate...
      • General Assembly
      • Newcomer's Day
      • CLAND / SAPS
      • IPCC report & Students
    • 2019
      • IPCC Report on Land
      • 4 per 1000 initiative
      • ISIMIP II
      • ISIMIP
    • 2018
      • Forecasting Crop Yields
      • Soil Carbon Sequestration

Breadcrumb

  • Home
  • Machine Learning

Machine learning for the study of climate and its impact - December 8, 2020


machinelearning

 

CLAND Convergence Institute has organized on December 8, 2020 by videoconference a workshop dedicated to “Machine learning for the study of climate and its impact”.

Machine learning is increasingly being used in climate science for a variety of applications, including greenhouse gas emissions, agricultural yield forecasting, mapping plant and soil characteristics, and emulating process-based models. This workshop brought together ten international speakers who presented machine learning applications to solve important problems related to climate change. 

 

Find all the workshop presentations below
 

click-here2

 

DOI-logo

Please note that the videos and presentations available here are protected by a CC-BY-SA license.
This means you may not use the material for commercial purposes and must give appropriate credit.
More information  

 

 

Machine learning as a framework for spatial and spatiotemporal
prediction in environmental and earth sciences: the dos and don'ts

Tomislav HENGL

[PDF]

 

Machine learning to predict peatland greenhouse gas emissions
Yuanyuan HUANG

[PDF]

 

Creating spatial insights into global-scale model inaccuracies
Johan VAN DEN HOOGEN

[PDF]

 

The decreasing vulnerability of French crop production to climatic hazards
Bernhard SCHAUBERGER

[PDF]

 

Walking through a random Forest: how to build, select, and inspect
a model of global forest symbioses?

Brian STEIDINGER

[PDF]
 

The implementation of machine learning algorithms in the prediction of
crop production in conservation agriculture under climate change

Yang SU

[PDF]
 

Tracking methane emissions globally using hyperspectral imaging
Alex D'ASPREMONT

[PDF]

 

A machine learning method to understand the primary climatic
drivers of wheat yield shocks across Europe

Peng ZHU

[PDF]

 

Machine learning emulators reveal divergence in soil carbon
controls between observations and process-based models

Katerina GEORGIOU

[PDF]

 

Recent advances in deep learning applications in climate science:
pattern recognition, system emulation, downscaling and forecasting

Karthik KASHINATH

[PDF]
Université Paris-Saclay ANR

<3

 

Footer menu

  • Contact

@ Cland