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    Author(s): Daniel L. Schmoldt
    Date: 2001
    Source: Risk Analysis in Forest Management. 49-74.
    Publication Series: Miscellaneous Publication
    PDF: View PDF  (156.22 KB)


    Forest ecosystems are subject to a variety of natural and anthropogenic disturbances that extract a penalty from human population values. Such value losses (undesirable effects) combined with their likelihoods of occurrence constitute risk. Assessment or prediction of risk for various events is an important aid to forest management. Artificial intelligence (AI) techniques have been applied to risk analysis owing to their ability to deal with uncertainty, vagueness, incomplete and inexact specifications, intuition, and qualitative information. This paper examines knowledge-based systems, fuzzy logic, artificial neural networks, and Bayesian belief networks and their application to risk analysis in the context of forested ecosystems. Disturbances covered are: fire, insects/diseases, meteorological, and anthropogenic. Insect/disease applications use knowledge-based system methods exclusively, whereas meteorological applications use only artificial neural networks. Bayesian belief network applications are almost nonexistent, even though they possess many theoretical and practical advantages. Embedded systems -that use AI alongside traditional methods are, not unexpectedly, quite common.

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    Schmoldt, Daniel L. 2001. Application of artificial intelligence to risk analysis for forested ecosystems. Risk Analysis in Forest Management. 49-74.

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