Machine learning for the study of climate and its impact - December 8, 2020
CLAND Convergence Institute is pleased to organise on December 8, 2020 from 13:00 to 17:00 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 brings together ten international speakers who present machine learning applications to solve important problems related to climate change. Each speaker will present his or her project for 15 minutes, focusing on the technical aspects of the study, with 5 minutes for questions afterwards.
Already confirmed speakers:
- Alex D'ASPREMONT (ENS) :
Tracking Methane Emissions Globally using Hyperspectral Imaging - Katerina GEORGIOU (Stanford University) :
Recent advances in deep learning applications in climate science: pattern recognition, system emulation, downscaling and forecasting - Tomislav HENGL (OpenGeoHub Foundation) :
Machine Learning as a framework for spatial and spatiotemporal prediction in Environmental and Earth Sciences: The Dos and Don'ts - Yuanyuan HUANG (CSIRO) :
Machine learning to predict peatland greenhouse gas emissions - Karthik KASHINATH (Lawrence Berkeley National Laboratory) :
Recent advances in deep learning applications in climate science: pattern recognition, system emulation, downscaling and forecasting - Bernhard SHAUBERGER (PIK Potsdam) :
The decreasing vulnerability of French crop production to climatic hazards - Brian STEIDINGER (Stanford University) :
Walking through a randomForest: how to build, select, and inspect a model of global forest symbioses - Yang SU (LSCE) :
The implementation of machine learning algorithms in the prediction of crop production in conservation agriculture under climate change - Johan VAN DEN HOOGEN (ETH Zürich) :
Creating spatial insights into global-scale model inaccuracies - Peng ZHU (LSCE) :
A machine learning method to understand the primary climatic drivers of wheat yield shocks across Europe
