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Non-parametric modelling in Geoscience: Application, optimization and uncertainty estimation - Softcover
Synopsis
In Geosciences inferences on processes are usually derived from irregularly measured data in both space and time. Such empirical time series are often characterized by changing boundary conditions, nonlinearity and uncertainty. Hence, phenomenological knowledge of processes is indispensable in order to derive physically meaningful equations describing the underlying dynamical system. However, unambiguous inferences prove to be difficult when nonlinearity and non-stationarity are present. In contrast to numerical models, data-driven models are specifically built to be parsimonious with a minimal set of adjustable parameters, intended to reproduce the statistical properties of signals. This dissertation focuses on the fundamental aspects of uncertainty estimation of nonlinear data-driven prediction methods. Within this framework, the essential factors in the model development process are emphasized and discussed on the basis of both climatological and hydrological time series. A key issue of the thesis is whether the analysis of uncertainties might contribute to process understanding and eventually support the optimization of data-driven models.
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About the Author
The author was born 1977 in Aalen, Germany. He received his PhD in Physical Geography from the RWTH Aachen University in 2011. Currently he holds a postdoc position at the Department of Geography ,RWTH University (Physical Geography and Climatology Group).
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- PublisherSüdwestdeutscher Verlag für Hochschulschriften
- Publication date2012
- ISBN 10 3838131061
- ISBN 13 9783838131061
- BindingPaperback
- LanguageEnglish
- Number of pages160
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Non-parametric modelling in Geoscience
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Südwestdeutscher Verlag Für Hochschulschriften Feb 2012, 2012
ISBN 10: 3838131061
ISBN 13: 9783838131061
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In Geosciences inferences on processes are usually derived from irregularly measured data in both space and time. Such empirical time series are often characterized by changing boundary conditions, nonlinearity and uncertainty. Hence, phenomenological knowledge of processes is indispensable in order to derive physically meaningful equations describing the underlying dynamical system. However, unambiguous inferences prove to be difficult when nonlinearity and non-stationarity are present. In contrast to numerical models, data-driven models are specifically built to be parsimonious with a minimal set of adjustable parameters, intended to reproduce the statistical properties of signals. This dissertation focuses on the fundamental aspects of uncertainty estimation of nonlinear data-driven prediction methods. Within this framework, the essential factors in the model development process are emphasized and discussed on the basis of both climatological and hydrological time series. A key issue of the thesis is whether the analysis of uncertainties might contribute to process understanding and eventually support the optimization of data-driven models. 160 pp. Englisch. Seller Inventory # 9783838131061
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Non-parametric modelling in Geoscience : Application, optimization and uncertainty estimation
Published by
Südwestdeutscher Verlag Für Hochschulschriften, 2012
ISBN 10: 3838131061
ISBN 13: 9783838131061
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In Geosciences inferences on processes are usually derived from irregularly measured data in both space and time. Such empirical time series are often characterized by changing boundary conditions, nonlinearity and uncertainty. Hence, phenomenological knowledge of processes is indispensable in order to derive physically meaningful equations describing the underlying dynamical system. However, unambiguous inferences prove to be difficult when nonlinearity and non-stationarity are present. In contrast to numerical models, data-driven models are specifically built to be parsimonious with a minimal set of adjustable parameters, intended to reproduce the statistical properties of signals. This dissertation focuses on the fundamental aspects of uncertainty estimation of nonlinear data-driven prediction methods. Within this framework, the essential factors in the model development process are emphasized and discussed on the basis of both climatological and hydrological time series. A key issue of the thesis is whether the analysis of uncertainties might contribute to process understanding and eventually support the optimization of data-driven models. Seller Inventory # 9783838131061
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Non-parametric modelling in Geoscience
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ISBN 10: 3838131061
ISBN 13: 9783838131061
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sauter TobiasThe author was born 1977 in Aalen, Germany. He received his PhD in Physical Geography from the RWTH Aachen University in 2011. Currently he holds a postdoc position at the Department of Geography ,RWTH University (Physic. Seller Inventory # 5407391
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