| Modeling of Trophospheric Ozone Concentrations Using Genetically Trained Multi-Level Cellular Neural Networks |
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| 作者姓名： | H. Kurtulus OZCAN Erdem BILGILI Ulku SAHIN O. Nuri UCAN Cuma BAYAT |
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| 作者单位： | Istanbul University Engineering Faculty Environmental Eng.Dept.34320，Avcilar，Istanbul，Turkey，Tuzla Marine Education Center Tuzla，Istanbul，Turkey，Istanbul University Engineering Faculty，Environmental Eng.Dept.34320，Avcilar，Istanbul，Turkey，Istanbul University Engineering Faculty，Electrical-Electronics Eng.Dept.34320，Avcilar，Istanbul，Turkey，Beykent University 34500 Buyukcekmece，Istanbul |
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| 摘 要： | 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.
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| 关 键 词： | 遗传算法 细胞神经网络 臭氧浓度 气象数据 |
| 收稿时间： | 12 September 2006 |
| 修稿时间： | 2006-09-12 |
Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks |
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| H. Kurtulus OZCAN,Erdem BILGILI,Ulku SAHIN,O. Nuri UCAN,Cuma BAYAT.Modeling of Trophospheric Ozone Concentrations Using Genetically Trained Multi-Level Cellular Neural Networks[J].Advances in Atmospheric Sciences,2007,24(5):907-914. |
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| Authors: | H Kurtulus Ozcan Erdem Bilgili Ulku Sahin O Nuri Ucan Cuma Bayat |
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| Institution: | Istanbul University, Engineering Faculty, Environmental Eng. Dept. 34320, Avcilar, Istanbul, Turkey,Tuzla Marine Education Center, Tuzla, Istanbul, Turkey,Istanbul University, Engineering Faculty, Environmental Eng. Dept. 34320, Avcilar, Istanbul, Turkey,Istanbul University, Engineering Faculty, Electrical-Electronics Eng. Dept. 34320, Avcilar, Istanbul, Turkey,Beykent University 34500, Buyukcekmece, Istanbul |
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| Abstract: | 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. |
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| Keywords: | genetic algorithm cellular neural networks (CNN) ozone meteorological data |
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