Comparative study of fuzzy-AHP and BBN for spatially-explicit prediction of bark beetle predisposition

Abstract The European spruce bark beetle ‘Ips typographus L.’ is the most serious disturbance agent for European forests. The complex interactions of many influencing factors need to be integrated into a model-based decision-support system to reduce the potential loss of forests. This paper compares two methodological approaches for spatially-explicit prediction of the predisposition for bark beetle infestations. The fuzzy analytic hierarchy process and the Bayesian belief networks were used in combination with a geographical information system to manage uncertainties. Using available data resources, the two approaches were evaluated to produce robust results for forest practitioners and to support measures to minimize the spread of bark beetles. The findings revealed that nearly 32% of the sites investigated in a case study were moderately-high or high risk categories. It is concluded that BBN is more efficient. Both methods can easily be used to analyze environmental problems involving complex interactions among various criteria.