Comparison of Dispersion Models by Using Fuzzy Similarity Relations

Aim of this work is to introduce a methodology, based on the combination of multiple temporal hierarchical agglomerations, for model comparisons in a multi-model ensemble context. We take advantage of a mechanism in which hierarchical agglomerations can easily combined by using a transitive consensus matrix. The hierarchical agglomerations make use of fuzzy similarity relations based on a generalized Lukasiewicz structure. The methodology is used to analyze data from a multi-model air quality ensemble system. The models are operational long-range transport and dispersion models used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides in the atmosphere. We apply the described methodology to agglomerate and to individuate the models that characterize the predicted atmospheric pollutants from the ETEX-1 experiment

Comparison of Dispersion Models by Using Fuzzy Similarity Relations

Aim of this work is to introduce a methodology based on the combination of multiple temporal hierarchical agglomerations by using fuzzy similarity relations for model comparison in a multi-model ensem- ble context. The similarity is a fuzzy relation and it is a generalization of the classic equivalence relation. In this work we consider a generalization of the fuzzy similarity considering a generalized Lukasiewicz structure. Moreover we introduce a mechanism in which multiple temporal hierar- chical agglomerations can be easily combined by using a transitive consensus matrix. The methodology is used to analyze data from a multi-model air quality ensemble system. The models are operational long-range trans- port and dispersion models used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides in the atmo- sphere. We apply the described methodology to agglomerate distributions of predicted atmospheric pollutants from the ETEX-1 experiment.JRC.H.2-Climate change and air qualit