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Updated: Jun 27, 2026
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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
Published on: October 11, 2016
Knowledge-based risk assessment under uncertainty for species invasion
Iftikhar U Sikder1, Sanchita Mal-Sarkar, Tarun K Mal
1Department of Computer and Information Science, Cleveland State University, Cleveland, OH 44115, USA. iftikhar.sikder@gmail.com
Risk Analysis : an Official Publication of the Society for Risk Analysis
|February 24, 2006
Summary
This study introduces a novel dominance-based rough set approach for invasive species risk assessment, improving predictions under uncertainty by integrating expert knowledge and spatial data for better management strategies.
Area of Science:
- Ecology
- Environmental Science
- Risk Assessment
Background:
- Effective invasive species management requires accurate risk assessment frameworks.
- Uncertainty in invasion processes complicates exposure analysis and risk modeling.
- Traditional models struggle with incomplete, imprecise expert knowledge and preference-ordered criteria.
Purpose of the Study:
- To present a novel dominance-based rough set approach for invasive species risk assessment.
- To integrate expert judgment and empirical data, accounting for preference-ordered attributes.
- To develop a knowledge-centric model for reasoning over invasion scenarios and spatial risk.
Main Methods:
- Utilized a dominance-based rough set approach to handle preference-ordered attributes in risk classes.
- Derived decision rules from a knowledge-centric model integrated with rough set principles.
- Extended rough set theory for spatial risk assessment using imprecise probability measures and neighborhood information.
Main Results:
- The model effectively incorporates preference order and uncertainty in risk assessment.
- Decision rules were derived to reason about potential invasion scenarios.
- Spatial context and multispecies interactions were approximated using belief and plausibility measures.
Conclusions:
- The dominance-based rough set approach offers a robust method for invasive species risk assessment under uncertainty.
- This framework enhances the integration of diverse knowledge sources for ecological risk management.
- The spatial extension provides a more comprehensive understanding of invasion risks.
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