Fuzzy Similarity-Based Hierarchical Clustering for Atmospheric Pollutants Prediction

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...

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Bibliographic Details
Published inFuzzy Logic and Applications Vol. 11291; pp. 123 - 133
Main Authors Camastra, F., Ciaramella, A., Son, L. H., Riccio, A., Staiano, A.
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2019
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Air pollutant dispersion
Ensemble models
Fuzzy similarity
Hierarchical agglomeration
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ISBN9783030125431
3030125432
ISSN0302-9743
1611-3349
DOI10.1007/978-3-030-12544-8_10

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Summary: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.
ISBN:9783030125431
3030125432
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-12544-8_10