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Universität Oldenburg

Data-adaptive reduction of process-based models

Abstract

dc:description.abstract

Process-based models of environmental systems typically are very complex structures. This complexity arises from the attempt to describe the manifold natural processes and intricate biological interactions of environmental systems in mathematical terms. Because of their high number of state variables and parameters, the resulting complex models are difficult to calibrate and detailed model analysis is needed to extract the key governing processes within these complex structures. Acknowledging these problems, the present thesis aims at finding a method to reduce complex process-based models. The main objectives for the development of the new method are its general applicability, its automated execution and its ability to construct reduced models which are interpretable in terms of system-specific mechanisms. The Mapping-based Genetic Reduction technique (MAGER) proposed in this thesis is a data-adaptive black-box procedure based on Genetic Programming which can be applied to ordinary differential equation models. In the course of this thesis, the MAGER scheme is applied to three predator-prey and consumer-resource models of different dimensionality. It is found that even relatively simple models can be reduced further and the results show that a formal conformity of physical and biological oscillating systems exists. In addition, the reduction of the biological systems involves a change in description level. Instead of traditional density or traits variables, the new models incorporate descriptions of biological interactions which leads to the notion of Ecological Interaction Models (EIM) for this new model class. The uniformity of the results further points to the generality of the EIM descriptions. As the MAGER scheme only depends on time series data and ignores former model structures, it is suggested that the method can also be applied to measured data or models from other scientific disciplines which offers many possibilities for further studies.

Degree

thesis:*
Level thesis:degree_level
thesis.doctoral
Grantor dc:publisher
Universität Oldenburg
Year
2008

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Bernhardt, Knut

Subjects

dc:subject × 1

Identifiers

dc:identifier.*
Repository record source_url
http://oops.uni-oldenburg.de/721
OAI identifier oai:identifier
oai:oops.uni-oldenburg.de:721

Chain of custody

source
Harvested from
Carl von Ossietzky Universität Oldenburg
Base URL
oops.uni-oldenburg.de/cgi/oai2
Last updated
2026-07-27
Source record
OAI-PMH GetRecord
citation

Bernhardt, Knut. Data-adaptive reduction of process-based models. thesis.doctoral thesis, Universität Oldenburg, 2008. http://oops.uni-oldenburg.de/721