This work focuses on models selection in 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. In this context, a methodology based on temporal hierarchical agglomeration is introduced. It uses fuzzy similarity relations combined by a transitive consensus matrix. The methodology is adopted for individuating a subset of models that best characterize the predicted atmospheric pollutants from the ETEX-1 experiment and discard redundant information.
|Titolo:||Fuzzy Similarity-Based Hierarchical Clustering for Atmospheric Pollutants Prediction|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|