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On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory : Springer Theses

Autor Fabian Guignard
en Limba Engleză Paperback – 13 mar 2023
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.
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Specificații

ISBN-13: 9783030952334
ISBN-10: 3030952339
Ilustrații: XVIII, 158 p. 68 illus., 43 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.28 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Springer Theses

Locul publicării:Cham, Switzerland

Cuprins

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.

Notă biografică

Dr. Fabian Guignard is an environmental data scientist born in 1983 in Switzerland. He received a M.S. degree in Mathematics from Ecole Polytechnique Fédérale de Lausanne (EPFL, Switzerland) in 2015 and a Ph.D. in Environmental Sciences from the University of Lausanne (UNIL, Switzerland) in 2021. His main research interests lie at the intersection of applied mathematics and computer science, including machine learning, uncertainty quantification and their applications to environmental spatio-temporal statistics.

Textul de pe ultima copertă

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.

Caracteristici

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