Systems and Control
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Environmental Science
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Abstract
Young, P.C. (1999) Data-based mechanistic modelling, generalised
sensitivity and dominant mode analysis. Computer Physics Communications,
117, 113-129
Since the inherent uncertainty associated with most environmental and
climatic systems is often acknowledged, it is surprising that most
mathematical models of such systems are large, complex and completely
deterministic in nature. In this situation, it seems sensible to consider
alternative modelling methodologies which overtly acknowledge the often
poorly defined nature of such systems and attempt to find simpler,
stochastic descriptions which are more appropriate to the often limited
data and information base. This paper considers one such approach,
Data-based Mechanistic (DBM) modelling, and demonstrates how it can be
useful not only for the modelling of environmental and other systems
directly from time series data, but also as an approach to the evaluation
and simplification of large deterministic simulation models. To achieve
these objectives, the DBM approach exploits various methodological tools,
including advanced methods of statistical identification and estimation; a
particular form of Generalised Sensitivity Analysis based on Monte-Carlo
Simulation; and Dominant Mode Analysis, the latter involving a new
statistical approach to combined model linearisation and order reduction.
These various techniques are outlined in the paper and they are applied to
the stochastic modelling of water pollution in rivers and the evaluation of
nonlinear global carbon cycle models.
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Updating responsibility Arun Chotai. This page is copyright of Lancaster University.
12/10/01 - PGM.
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