Application of artificial neural networks and regression models in the prediction of daily maximum PM10 concentration in Düzce, Tukey
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Dosyalar
Tarih
2014
Yazarlar
Dergi Başlığı
Dergi ISSN
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Yayıncı
Parlar Scientific Publications
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Increasing levels of atmospheric particulate matter are known to adversely affect human health. Therefore, air quality predictions may provide important information in order to take actions for the public before the pollution happens. In this study, we presented artificial neural network (ANN), stepwise regression (SR) and multiple linear regression (MLR) models to forecast maximum daily PM1o concentrations one day ahead in Düzce, Turkey. Particularly, a special emphasis was put on the prediction of particulate levels during winter episodes. Inputs to the models include lagged values of maximum, minimum and standard deviations of PM1o concentrations, and some meteorological factors, which are all on daily basis. The output is the expected maximum concentration of PM10 in (?g.-3 for the following day. The data sets used in training and testing stages covered the daily averaged values of these variables for the period of 2011-2013. The results showed that selected inputs based on stepwise regression approach and use of cascading-training in multi-layer perceptron ANN (ANN-MLP) appeared to be promising with R2 up to 0.69 and index-of-agreement up to 0.79. It is concluded that local monitoring systems associated with ANN model predictions may be a sound way to develop embedded online systems for public health. © by PSP.
Açıklama
Anahtar Kelimeler
Artificial neural networks; Forecasting; Multiple regression; Particulate matter; Stepwise regression
Kaynak
Fresenius Environmental Bulletin
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
23
Sayı
10
