Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks

Özcan, Hüseyin; Bilgili, Erdem; Şahin, Ülkü Alver; Uçan, Osman N.; Bayat, Cuma

doi:10.1007/s00376-007-0907-y

Cited by 11 publications

(9 citation statements)

References 15 publications

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“…2). A good generalization performance was obtained since there were not significant differences between Ozcan et al [27] have reported the values of MAE and RMSE as 6.32 and 8.70 mg/m 3 , respectively for the prediction of next-day daily mean ozone concentrations in Istanbul using cellular neural network approach. However, their correlation coefficient (R ¼ 0.57) was lower than the one we obtained in this study.…”

Section: Resultsmentioning

confidence: 88%

“…Different approaches have been used to model tropospheric ozone concentrations in Istanbul: non-linear time series method [24], regression model [25], fuzzy synthetic evaluation techniques [26], and cellular neural networks [27]. Ozcan et al [27] have utilized genetically trained, multi-level cellular neural network to predict ozone values 24 h in advance.…”

Section: Introductionmentioning

confidence: 99%

“…Ozcan et al [27] have utilized genetically trained, multi-level cellular neural network to predict ozone values 24 h in advance. The input parameters for the model were meteorological and air pollutant data for the year 2003 (January to December).…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey

İnal

2010

CLEAN Soil Air Water

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Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, TurkeyTropospheric (ground-level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, crossvalidation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d 2 ) for the testing data were 0.90, 8.78 mg/m 3 , 11.15 mg/m 3 , and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM 10 , maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d 2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non-linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul. IntroductionOzone (O 3 ) is a secondary pollutant and formed in the lower atmosphere (troposphere) by the complex reactions of nitrogen oxides (NO x ) and volatile organic compounds (VOCs) in the presence of solar radiation. Ozone formation in the troposphere is a rapid photochemical cycle and a non-linear process depending on the concentrations of precursors, meteorological parameters, and the sunlight intensity and spectral distribution [1,2]. Briefly, it involves the photolysis of nitrogen dioxide (NO 2 ) by solar radiation to form nitric oxide (NO) and a ground-state oxygen atom (O).The major reaction forming O 3 in the troposphere includes the reaction of oxygen atom with oxygen molecule (O 2 ). Collision of recently formed O 3 with a third body (i.e., a molecule from the surrounding air) removes the excess energy of ozone and allows it to stabilize.O 3 is removed by the reaction with NO to reform NO 2 ;However, reactions between NO and reactive radical species that are formed by the oxidation of reactive VOCs can also oxidize NO to NO 2 without the involvement of ozone, NO ÀÀÀÀÀÀÀ!

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Section: Resultsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey

İnal

2010

CLEAN Soil Air Water

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“…Recent meteorological evaluations, and tracer and trajectory studies have indicated that trans-boundary transport of air pollutants from Europe are also responsible for the poor air quality of Istanbul under specific weather conditions [23]. Different approaches have been used to model tropospheric ozone concentrations in Istanbul: non-linear time series method [24], regression model [25], fuzzy synthetic evaluation techniques [26], and cellular neural networks [27]. Ozcan et al [27] have utilized genetically trained, multi-level cellular neural network to predict ozone values 24 h in advance.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey

Inal¹

2010

CLEAN Soil Air Water

View full text Add to dashboard Cite

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, TurkeyTropospheric (ground-level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, crossvalidation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d 2 ) for the testing data were 0.90, 8.78 mg/m 3 , 11.15 mg/m 3 , and 0.95, respectively.Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM 10 , maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d 2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non-linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul. IntroductionOzone (O 3 ) is a secondary pollutant and formed in the lower atmosphere (troposphere) by the complex reactions of nitrogen oxides (NO x ) and volatile organic compounds (VOCs) in the presence of solar radiation. Ozone formation in the troposphere is a rapid photochemical cycle and a non-linear process depending on the concentrations of precursors, meteorological parameters, and the sunlight intensity and spectral distribution [1,2]. Briefly, it involves the photolysis of nitrogen dioxide (NO 2 ) by solar radiation to form nitric oxide (NO) and a ground-state oxygen atom (O).The major reaction forming O 3 in the troposphere includes the reaction of oxygen atom with oxygen molecule (O 2 ). Collision of recently formed O 3 with a third body (i.e., a molecule from the surrounding air) removes the excess energy of ozone and allows it to stabilize.O 3 is removed by the reaction with NO to reform NO 2 ;However, reactions between NO and reactive radical species that are formed by the oxidation of reactive VOCs can also oxidize NO to NO 2 without the involvement of ozone,

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Fuzzy Inference ANN Ensembles for Air Pollutants Modeling in a Major Urban Area: The Case of Athens

Bougoudis

Iliadis

Papaleonidas

2014

Engineering Applications of Neural Networks

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Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks

Cited by 11 publications

References 15 publications

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey

Fuzzy Inference ANN Ensembles for Air Pollutants Modeling in a Major Urban Area: The Case of Athens

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