Fabian Guignard
On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
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- More about On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
The growth of data indexed in space and time requires advanced tools to analyze them. This thesis proposes a framework for modeling with machine learning algorithms and uncertainty quantification using the extreme learning machine and Fisher-Shannon analysis. Examples include wind speed modeling and renewable energy assessment in environmental sciences.
Format: Hardback
Length: 158 pages
Publication date: 13 March 2022
Publisher: Springer Nature Switzerland AG
The exponential growth of data indexed in space and time is presenting a significant challenge, requiring advanced and tailored tools to analyze it effectively. This thesis delves into the exploration and modeling of intricate high-frequency and non-stationary spatio-temporal data. It proposes an efficient framework for modeling spatio-temporal fields measured on irregular monitoring networks, accounting for the high-dimensional input space and large data sets. The uncertainty quantification is made possible by incorporating the extreme learning machine, a specialized type of artificial neural network, which enables analytical results, variance estimation, and confidence intervals. Additionally, the thesis emphasizes a versatile exploratory data analysis tool based on information theory, the Fisher-Shannon analysis, which serves to evaluate the complexity of distributional properties of temporal, spatial, and spatio-temporal data sets.
Examples of the proposed methodologies are focused on data from environmental sciences, particularly emphasizing wind speed modeling in complex mountainous terrain and the subsequent assessment of renewable energy potential. The contributions of this thesis have broad applications in various research domains where the exploration, comprehension, clustering, interpolation, and forecasting of complex phenomena are of paramount importance.
Weight: 436g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030952303
Edition number: 1st ed. 2022
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