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

Guignard, Fabian

Hardcover

ISBN 10: 3030952304 ISBN 13: 9783030952303

Verlag: Springer, 2022

Inhaltsangabe

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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Über die Autorin bzw. den Autor

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.

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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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2022
• Sprache: Englisch
• ISBN 10: 3030952304
• ISBN 13: 9783030952303
• Einband: Gebundene Ausgabe
• Auflage: 1
• Anzahl der Seiten: 176
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Deutschland