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8.911 Lectures/texts in conference proceedings
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Advances in machine learning for modelling and understanding in earth sciences

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Camps-Valls, Gustau
Document typeConference report
Defense date2020
PublisherBarcelona Supercomputing Center
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
The Earth is a complex dynamic network system. Modelling and understanding the system is at the core of scientific endeavour. We approach these problems with machine learning algorithms. I will review several ML approaches we have developed in the last years: 1) advanced Gaussian processes models for bio-geo-physical parameter estimation, which can incorporate physical laws, blend multisensor data while providing credible confidence intervals for the estimates and improved interpretability, 2) nonlinear dimensionality reduction methods to decompose Earth data cubes in spatially-explicit and temporally-resolved modes of variability that summarize the information content of the data and allow for identifying relations with physical processes, and 3) advances in causal inference that can uncover cause and effect relations from purely observational data.
CitationCamps-Valls, G. Advances in machine learning for modelling and understanding in earth sciences. A: . Barcelona Supercomputing Center, 2020, p. 51-52. 
URIhttp://hdl.handle.net/2117/330962
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