On spatio-temporal data modelling and uncertainty quantification using machine learning and information theory / Fabian Guignard.
2022
Q375
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Details
Title
On spatio-temporal data modelling and uncertainty quantification using machine learning and information theory / Fabian Guignard.
Author
Guignard, Fabian, author.
ISBN
9783030952310 (electronic bk.)
3030952312 (electronic bk.)
9783030952303 (print)
3030952304
3030952312 (electronic bk.)
9783030952303 (print)
3030952304
Published
Cham, Switzerland : Springer, 2022.
Language
English
Description
1 online resource (xviii, 158 pages) : illustrations (some color).
Other Standard Identifiers
10.1007/978-3-030-95231-0 doi
Call Number
Q375
Dewey Decimal Classification
003/.54
Summary
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.
Note
"Doctoral Thesis accepted by University of Lausanne, Switzerland."
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed March 16, 2022).
Series
Springer theses, 2190-5061
Available in Other Form
Print version: 9783030952303
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Table of Contents
Introduction
Study Area and Data Sets
Advanced Exploratory Data Analysis
Fisher-Shannon Analysis
Spatio-Temporal Prediction with Machine Learning
Uncertainty Quantification with Extreme Learning Machine
Spatio-Temporal Modelling using Extreme Learning Machine
Conclusions, Perspectives and Recommendations.
Study Area and Data Sets
Advanced Exploratory Data Analysis
Fisher-Shannon Analysis
Spatio-Temporal Prediction with Machine Learning
Uncertainty Quantification with Extreme Learning Machine
Spatio-Temporal Modelling using Extreme Learning Machine
Conclusions, Perspectives and Recommendations.