Application Of Artificial Neural Networks For
Classification And Prediction Of Air Quality
Classes
Author(s)
J. Skrzypski, K. Kamiński, E. Jach-Szakiel & W. Kamiński
Abstract
In this study, the results of investigations which enable an extension of the
mathematical methods supporting air quality management in cities are presented.
The actions were focused on the development of neural models of classification
and prediction of the air quality classes (in respect of PM10 dust concentrations).
The air quality class on a following day was predicted. The aim of modelling
was to predict the air quality classes in the afternoon and in the evening when
PM10 concentrations attained the daily maxima. The monitoring of PM10
concentration and the meteorological data for winter periods in 2004–2007 was
used. The artificial neural network methods (ANN) with a simultaneous
application of data compression methods were tested. The results of the air
quality prediction are satisfactory. The accurate prognoses are predominant. The
percentage of wrong prognoses is relatively small. The investigations confirm
that neural prediction models allow good results to be obtained of the air quality
class prediction. The results of the research prove that the tested models may be
applied in the practice of air quality management in cities.
Keywords: classification, prediction, air quality, big cities, PM10, artificial
neural network, modelling.
Keywords
classification, prediction, air quality, big cities, PM10, artificial
neural network, modelling
Related Book
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