Article ID: | iaor20011947 |
Country: | Netherlands |
Volume: | 95 |
Issue: | 1 |
Start Page Number: | 341 |
End Page Number: | 364 |
Publication Date: | Jul 2000 |
Journal: | Annals of Operations Research |
Authors: | Mateos A., Ros-Insua S., Insua D. Ros, Gallego E. |
Keywords: | artificial intelligence: decision support, geography & environment, biology |
Interventions to restore radionuclide contaminated aquatic ecosystems may reduce individual and collective radiation doses, but may also result in detrimental ecological, social and economic effects. Decision makers must carefully evaluate possible impacts before choosing a countermeasure, hence decision analysis methods constitute an important aid to rank intervention strategies after the contamination of an aquatic ecosystem. We describe MOIRA, a decision support system for the identification of optimal remedial strategies to restore water systems after accidental introduction of radioactive substances. MOIRA includes an evaluation module based on a multi-attribute value model to rank alternatives and a module to perform multiparametric sensitivity analyses, both with respect to weights and values, to allow us to gain insights into the problem. The problem is under certainty since the validation of models used to quantify countermeasure impacts suggests little uncertainty in policy effects. The system is implemented in a PC based decision support system which allows the inclusion of all relevant information.