Authors:
Follows a comprehensive end-to-end data analysis workflow
Covers concrete applications in environmental modelling and renewable energy assessement
Benefits environmental analysts and researchers working with spatio-temporal data
Part of the book series: Springer Theses (Springer Theses)
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Table of contents (8 chapters)
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Front Matter
About this book
The gathering and storage of data indexed in space and time are experiencing unprecedented growth, demanding for advanced and adapted tools to analyse them. This thesis deals with the exploration and modelling of complex high-frequency and non-stationary spatio-temporal data. It proposes an efficient framework in modelling with machine learning algorithms spatio-temporal fields measured on irregular monitoring networks, accounting for high dimensional input space and large data sets. The uncertainty quantification is enabled by specifying this framework with the extreme learning machine, a particular type of artificial neural network for which analytical results, variance estimation and confidence intervals are developed. Particular attention is also paid to a highly versatile exploratory data analysis tool based on information theory, the Fisher-Shannon analysis, which can be used to assess the complexity of distributional properties of temporal, spatial and spatio-temporal data sets. Examples of the proposed methodologies are concentrated on data from environmental sciences, with an emphasis on wind speed modelling in complex mountainous terrain and the resulting renewable energy assessment. The contributions of this thesis can find a large number of applications in several research domains where exploration, understanding, clustering, interpolation and forecasting of complex phenomena are of utmost importance.
Keywords
- Machine Learning
- Deep Learning
- Uncertainty Quantification
- Model Variance
- Artificial Neural Network
- Statistical Complexity
- Environmental Modelling
- High-Frequency Wind Speed
Authors and Affiliations
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Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Fabian Guignard
About the author
Bibliographic Information
Book Title: On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
Authors: Fabian Guignard
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-030-95231-0
Publisher: Springer Cham
eBook Packages: Earth and Environmental Science, Earth and Environmental Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-95230-3Published: 13 March 2022
Softcover ISBN: 978-3-030-95233-4Published: 13 March 2023
eBook ISBN: 978-3-030-95231-0Published: 12 March 2022
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XVIII, 158
Number of Illustrations: 25 b/w illustrations, 43 illustrations in colour
Topics: Science, multidisciplinary, Simulation and Modeling, Renewable and Green Energy