D. Scott Mackay
Abstract
Global information systems have the potential of providing decision makers with timely spatial information about earth systems. This information will come from diverse sources, including field monitoring, remotely sensed imagery, and environmental models. Of the three the latter has the greatest potential of providing regional and global scale information on the behavior of environmental systems, which may be vital for setting multi-governmental policy and for making decisions that are critical to quality of life. However, environmental models have limited prootocol for quality control and standardization. They tend to have weak or poorly defined semantics and so their output is often difficult to interpret outside a very limited range of applications for which they are designed. This paper considers this issue with respect to spatially distributed environmental models. A method of measuring the semantic proximity between components of large, integrated models is presented, along with an example illustrating its application. It is concluded that many of the issues associated with weak model semantics can be resolved with the addition of self-evaluating logic and context-based tools that present the semantic weaknesses to the end-user.
Index Terms
- Semantic integration of environmental models for application to global information systems and decision-making
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