Ulrich Heller, Peter Struss
Conceptual Modeling in the Environmental Domain
Appeared in: 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics, Berlin, August 1997, ISBN 3-89685-556-5, Vol. 6, pp. 147-152, 1997.
Abstract
The environmental domain poses a number of important challenges
for the modeling task.
First, we have to deal with multiple dynamics that often
involve spatial variations. The time-scales of the relevant phenomena range
from long-term changes in climatic conditions to chemical reactions occurring
within parts of a second.
Second, we face largely incomplete knowledge in various
respects. The observability of ecosystems is limited, and we often have
to recur to sparse or imprecise measurements or even human observations.
Furthermore, the knowledge about the underlying phenomena and processes
is frequently of an inherently qualitative nature.
Aiming at an effective and coherent computational support
for the tasks of model building and revision as well as model usage for
prediction, diagnosis and planning, we propose the employment of a knowledge-based
approach at the conceptual level of modeling. An adequately abstract terminology
for description and reasoning about natural phenomena will use concepts
similar to the ones used by domain experts, such as processes, causal influences
and compartments.
Relying upon our experience in the modeling of algal
bloom phenomena in subtropical water bodies, we advocate a qualitative
formalism for the description of the influences of variables on each other.
We express various levels of abstraction of functional dependencies depending
on the knowledge available. The modeling language lends itself well to
the specification of model fragments (e. g. processes) that can be selected
according to their relevance and composed to form a prediction model. We
also carried out some research on automatic transformation of models obtained
in that way in order to account for separated time-scales, a technique
we have labeled time-scale approximation. Finally, the descriptions can
be conveniently visualized in the form of Qualitative Influence Diagrams
for supporting the communication with domain experts.
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