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

By I.N. Athanasiadis, V.G. Kaburlasos, P.A. Mitkas & V. Petridis
In First Int'l Symposium on Information Technologies in Environmental Engineering (ITEE-2003) , pp. 51 , Gdansk, Poland, 2003.

Abstract:
Cover image 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 σ-FLNMAP, for estimating the ozone concentration level in the atmosphere. In a series of experiments on meteorological and air pollutants data, the σ-FLNMAP classifier compares favorably with both back-propagation neural networks and the C4.5 algorithm; moreover σ-FLNMAP induces only a few rules from the data. The σ-FLNMAP classifier can be implemented as either a neural network or a decision tree. We also discuss the far reaching potential of σ-FLNMAP in IT applications due to its applicability on partially (lattice) ordered data.

Read also... Article (in pdf) - ITEE-03 - ICSC-NAISO