A Bayesian Belief Network Advisory System for Aspen Regeneration
Abstract:The probability-based Bayesian Belief Network (BBN) methodology is demonstrated to be an alternative to rule-based methods in forest management expert systems. Unlike a rule-based system, a BBN incorporates uncertainty in the knowledge and input data without sacrificing knowledge modularity. After reviewing the graph and probability theory needed to define a BBN as a joint distribution representable by a directed acyclic graph, an 11-variable network modeling Rocky Mountain aspen sucker density response to different management options is constructed. For a typical aspen site, the model's estimated marginal probabilities of sucker response exhibit values consistent with those expected. Finally, a BBN is shown to be fairly tolerant to small parameter errors, and a new method is given for BBN model validation. For. Sci. 37(2):627-654.
Document Type: Journal Article
Affiliations: Statistician, USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, CO 80526
Publication date: 1991-06-01
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- Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2015 Impact Factor: 1.702
Ranking: 16 of 66 in forestry
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Journal of Forestry
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