Abstract
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.
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Acknowledgments
This work was partially funded by the University of Naples Parthenope (Sostegno alla ricerca individuale per il triennio 2016–2018 project).
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Camastra, F., Ciaramella, A., Son, L.H., Riccio, A., Staiano, A. (2019). Fuzzy Similarity-Based Hierarchical Clustering for Atmospheric Pollutants Prediction. In: Fullér, R., Giove, S., Masulli, F. (eds) Fuzzy Logic and Applications. WILF 2018. Lecture Notes in Computer Science(), vol 11291. Springer, Cham. https://doi.org/10.1007/978-3-030-12544-8_10
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