AIR QUALITY FORECASTING FOR ALL SEASONS IN LARGE GEOGRAPHICAL AREAS

Year 2019, Volume: 20 Issue: 3, 365 - 372, 26.09.2019

Efnan Şora Günal

https://doi.org/10.18038/estubtda.545386

Abstract

Today,
air quality monitoring plays a vital role due to increasing number of
pollutants that threaten human health. Importance of providing accurate
information on air quality for forthcoming times is therefore very high. For
this purpose, many studies have been carried out to develop air quality
forecasting models. However, most of these studies focus on a particular season
and relatively small geographical areas. In this paper, unlike the previous
ones, an air quality forecasting model is proposed for all seasons in large
geographical areas. Turkiye that is a pretty large country where there are
seven distinct regions with different geographical and meteorological
characteristics is selected to apply the forecasting model. The proposed model categorizes
the upcoming 6-hour air quality level as “healthy”, “moderate” and “unhealthy”.
The model utilizes low and high order statistical features extracted from the
measurements of air quality monitoring stations covering most parts of the
geographical regions of Turkiye. The features are then fed into both linear and
non-linear classifiers including artificial neural networks, Fisher’s linear
discriminant analysis, nearest neighbor and Bayes classifier. Results of the
experimental study indicate that the proposed forecasting model is a promising
candidate to predict air quality through all seasons at relatively large
geographical areas with varying characteristics.

Keywords

Air pollution, Air quality index, Forecasting model

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