Forecasting ozone concentrations in the east of Croatia using nonparametric Neural Network Models (CROSBI ID 227800)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Kovač-Andrić, Elvira ; Sheta, Alaa ; Faris, Hossam ; Šrajer Gajdošik, Martina
engleski
Forecasting ozone concentrations in the east of Croatia using nonparametric Neural Network Models
Ozone is one of the most significant secondary pollutants with numerous negative effects on human health and environment including plants and vegetation. Therefore, more effort is made recently by governments and associations to predict ozone concentrations which could help in establishing better plans and regulation for environment protection. In this study, we use two Artificial Neural Network based approaches (MPL and RBF) to develop, for the first time, accurate ozone prediction models for one urban and one rural area in the Eastern part of Croatia. The evaluation of actual against the predicted ozone concentrations revealed that MLP and RBF models are very competitive for the training and testing data in the case of Kopački Rit area whereas in the case of Osijek city, MLP shows better evaluation results with 9% improvement in the correlation coefficient. Furthermore, subsequent feature selection process has improved the prediction power of RBF network.
Ozone ; PM10 ; rural and urban area ; prediction models ; Artificial Neural Networks.
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Podaci o izdanju
125 (5)
2016.
997-1006
objavljeno
0253-4126
10.1007/s12040-016-0705-y