Comparison of Self Organizing Maps Clustering with Supervised Classification for Air Pollution Data SetsReportar como inadecuado




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1 Democritus University of Thrace 2 Democritus University of Thrace

Abstract : Air pollution is a serious problem of modern urban centers. The objective of this research is to investigate the problem by using Machine Learning techniques. It comprises of two parts. Firstly, it applies a well established Unsupervised Machine Learning approach UML namely Self Organizing Maps SOM for the clustering of Attica air quality big data vectors. This is done by using the concentrations of air pollutants specific for each area for a period of 13-years 2000-2012. Secondly, it employs a Supervised Machine Learning methodology SML by using multi layer Artificial Neural Networks ML-ANN to classify the same cases. Actually, the ANN models are used to evaluate the SOM reliability. This is done, because there is no actual and well accepted clustering of the related data to compare with the outcome of the SOM and this adds innovation merit to this paper.

Keywords : Self Organizing Maps Artificial Neural Networks Classification Air Pollution





Autor: Ilias Bougoudis - Lazaros Iliadis - Stephanos Spartalis -

Fuente: https://hal.archives-ouvertes.fr/



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