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Auflistung nach Autor:in "Vlachogiannis, Diamando"

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  • Konferenzbeitrag
    A Data Mining Tool for the Analysis of Epidemiological Data
    (Managing Environmental Knowledge, 2006) Vlachogiannis, Diamando; Sfetsos, Athanasios
    The present paper introduces an integrated approach based on statistical analysis coupled with data mining to analyse epidemiological data. Initially, the statistical properties of the data are analysed. The causality of the exogenous variables (e.g. meteorological and air quality) on the epidemiological data through the Granger causality test is estimated in an attempt to identify those variables that explain major variations. Those variables that are estimated as important are subsequently binned into a finite number of categories as a pre-processing step for the data mining algorithm. The epidemiological and meteorological data are grouped into 5 categories, were as for the air quality parameters the Air Quality Index introduced by U.S. EPA is utilised. Then an algorithm to estimate association rules from the categorised data is developed and applied. The outcomes of the analysis are patterns that relate meteorological and air quality characteristics to specific epidemiological conditions and appear systematically on the examined data set. The application of the developed methodology is performed using data from two major U.S. cities, namely Los Angeles and Pittsburgh.
  • Konferenzbeitrag
    A methodology for the identification of weather types to assist air quality modelling from reservoir tracers emissions
    (Sh@ring – EnviroInfo 2004, 2004) Sfetsos, Athanasios; Vlachogiannis, Diamando; Gounaris, Nikos; Stubos, Athanasios; Tagaris, Efthimios; Pilinis, Christodoulos; Chatzichistos, Christos; Kleven, Reidun
    This paper presents a methodology for the determination of specific weather types and their representative days as a tool for the evaluation of atmospheric quality due to emissions of SF6 and PFCs during tracer reservoir technology programs in the North Sea. The methodology utilises observed and model derived information to identify groups of days that have common characteristics. The proposed approach is based on a rotated principal component analysis (r-PCA) module and the subtractive clustering algorithm. Subsequently, the transitional properties of successive days are examined in an attempt to identify weather characteristics of the examined area. The selected days were modelled with the 3-D photochemical model UAM-AERO, under different emissions scenarios. Atmospheric background concentrations of PFC and SF6 are not affected by the emissions of the oilrigs in the scenarios compiled with realistic emissions.