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Meta-Modeling: A Knowledge-Based Approach to Facilitating Model Construction and ReuseIn this paper, we introduce a new modeling approach called meta-modeling and illustrate its practical applicability to the construction of physically-based ecosystem process models. As a critical adjunct to modeling codes meta-modeling requires explicit specification of certain background information related to the construction and conceptual underpinnings of a model. This information formalizes the heretofore tacit relationship between the mathematical modeling code and the underlying real-world phenomena being investigated, and gives insight into the process by which the model was constructed. We show how the explicit availability of such information can make models more understandable and reusable and less subject to misinterpretation. In particular, background information enables potential users to better interpret an implemented ecosystem model without direct assistance from the model author. Additionally, we show how the discipline involved in specifying background information leads to improved management of model complexity and fewer implementation errors. We illustrate the meta-modeling approach in the context of the Scientists' Intelligent Graphical Modeling Assistant (SIGMA) a new model construction environment. As the user constructs a model using SIGMA the system adds appropriate background information that ties the executable model to the underlying physical phenomena under investigation. Not only does this information improve the understandability of the final model it also serves to reduce the overall time and programming expertise necessary to initially build and subsequently modify models. Furthermore, SIGMA's use of background knowledge helps eliminate coding errors resulting from scientific and dimensional inconsistencies that are otherwise difficult to avoid when building complex models. As a. demonstration of SIGMA's utility, the system was used to reimplement and extend a well-known forest ecosystem dynamics model: Forest-BGC.
Document ID
20020062769
Acquisition Source
Ames Research Center
Document Type
Preprint (Draft being sent to journal)
Authors
Keller, Richard M.
(NASA Ames Research Center Moffett Field, CA United States)
Dungan, Jennifer L.
(Johnson Controls, Inc. Moffett Field, CA United States)
Date Acquired
August 20, 2013
Publication Date
January 1, 1997
Subject Category
Numerical Analysis
Funding Number(s)
PROJECT: RTOP 632-30-00
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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