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    Author(s): Keith Reynolds; Patrick Cunningham; Larry Bednar; Michael Saunders; Michael Foster; Richard Olson; Daniel Schmoldt; Donald Latham; Bruce Miller; John Steffenson
    Date: 1996
    Source: AI Applications. 10(2): 9-22.
    Publication Series: Miscellaneous Publication
    PDF: View PDF  (193 KB)

    Description

    The Pacific Northwest Research Station (USDA Forest Service) is developing a knowledge-based information management system to provide decision support for watershed analysis. The system includes: (1) a GIS interface that allows users to navigate graphically to specific provinces and watersheds and display a variety of themes (vegetation, streams, roads, topography, etc.) and other area-specific information (relevant regulations, existence and location of analyses, plans, etc.); (2) an analysis component that helps identify major concerns and the hierarchies of associated ecosystem processes requiring analysis; (3) a report manager that displays the history, status, and details of analyses; (4) a project manager that assists with planning and monitoring of data acquisition; and (5) a hypermedia system that provides powerful navigation tools for accessing information in various policy and procedure documents. The analysis component contains dependency networks that link problem-solving knowledge about concerns, ecosystem processes, and data to specific landscape units. The goal-dependency approach provides a scientifically sound method for determining data requirements, as well as a basis for prioritizing, acquiring, and evaluating information for watershed analyses.

    Publication Notes

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    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    Reynolds, Keith; Cunningham, Patrick; Bednar, Larry; Saunders, Michael; Foster, Michael; Olson, Richard; Schmoldt, Daniel; Latham, Donald; Miller, Bruce; Steffenson, John. 1996. A Knowledge-Based Information Management System for Watershed Analysis in the Pacific Northwest U.S. AI Applications. 10(2): 9-22.

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