Applying machine learning techniques on air quality data for real-time decision support feature image Photo Credit: Ioannis N. Athanasiadis
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Ioannis Athanasiadis

Assistant Professor with the Democritus University of Thrace, in Xanthi, Greece

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Applying machine learning techniques on air quality data for real-time decision support

- I. N. Athanasiadis - V. G. Kaburlasos - P. A. Mitkas - V. Petridis -

Abstract: Fairly rapid environmental changes call for continuous surveillance and decision making, areas where IT technologies can be valuable. In the aforementioned context this work describes the application of a novel classifier, namely greektext σ-FLNMAP, for estimating the ozone concentration level in the atmosphere. In a series of experiments on meteorological and air pollutants data, the greektext σ-FLNMAP classifier compares favorably with both back-propagation neural networks and the C4.5 algorithm; moreover greektext σ-FLNMAP induces only a few rules from the data. The greektext σ-FLNMAP classifier can be implemented as either a neural network or a decision tree. We also discuss the far reaching potential of greektext σ-FLNMAP in IT applications due to its applicability on partially (lattice) ordered data.


Published as:
I. N. Athanasiadis, V. G. Kaburlasos, P. A. Mitkas, V. Petridis, Applying machine learning techniques on air quality data for real-time decision support, 1st Intl Symp on Information Technologies in Environmental Engineering (ITEE-2003), pg. 51, 2003, ICSC-NAISO Academic Press.