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Knowledge-Based Risk Assessment Under Uncertainty for Species Invasion

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Abstract:

Management of invasive species depends on developing prevention and control strategies through comprehensive risk assessment frameworks that need a thorough analysis of exposure to invasive species. However, accurate exposure analysis of invasive species can be a daunting task because of the inherent uncertainty in invasion processes. Risk assessment of invasive species under uncertainty requires potential integration of expert judgment with empirical information, which often can be incomplete, imprecise, and fragmentary. The representation of knowledge in classical risk models depends on the formulation of a precise probabilistic value or well-defined joint distribution of unknown parameters. However, expert knowledge and judgments are often represented in value-laden terms or preference-ordered criteria. We offer a novel approach to risk assessment by using a dominance-based rough set approach to account for preference order in the domains of attributes in the set of risk classes. The model is illustrated with an example showing how a knowledge-centric risk model can be integrated with the dominance-based principle of rough set to derive minimal covering “if … , then…,” decision rules to reason over a set of possible invasion scenarios. The inconsistency and ambiguity in the data set is modeled using the rough set concept of boundary region adjoining lower and upper approximation of risk classes. Finally, we present an extension of rough set to evidence a theoretic interpretation of risk measures of invasive species in a spatial context. In this approach, the multispecies interactions in an invasion risk are approximated with imprecise probability measures through a combination of spatial neighborhood information of risk estimation in terms of belief and plausibility.

Keywords: Decision rules; dominance-based rough set; inconsistent and ambiguous data; invasive species; uncertain reasoning

Document Type: Research Article

DOI: https://doi.org/10.1111/j.1539-6924.2006.00714.x

Affiliations: 1: Department of Computer and Information Science, Cleveland State University, Cleveland, OH 44115, USA. 2: Department of Biological, Geological and Environmental Sciences, Cleveland State University, Cleveland, USA.

Publication date: 2006-02-01

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