Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks
Küçük Resim Yok
Tarih
2007
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Science China Press
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.
Açıklama
Anahtar Kelimeler
genetic algorithm, cellular neural networks (CNN), ozone, meteorological data
Kaynak
Advances In Atmospheric Sciences
WoS Q Değeri
Q4
Scopus Q Değeri
N/A
Cilt
24
Sayı
5
