Technical University of Munich, Department of Computer Science
MQM Paper: [Heller/Struss 97]

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.


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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created: Ulrich Heller, May 6, 1997, last updated: Jakob Mauss, December 4, 1998