Nonlinear time series prediction of O3 concentration in Istanbul

Koçak, Kasım; Şaylan, Levent; Şen, Orhan

doi:10.1016/s1352-2310(99)00323-4

Cited by 62 publications

(29 citation statements)

References 13 publications

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“…Its applications in many studies were done to describe the past movement of particular variable with respect to time. However, there were several different techniques applied by researcher so that the change of air pollution behavior through time period can be determined [6,7]. A study by Kuang-Jung Hsu,2003 was done by using autoregression variation (VAR) in order to establish interdependence between primary and secondary air pollutants in area of Taipei.…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis of Surface Ozone Monitoring Records in Kemaman, Malaysia

Ismail¹,

Ibrahim²,

Ibrahim³

et al. 2012

Air Pollution - A Comprehensive Perspective

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Section: Introductionmentioning

confidence: 99%

Time Series Analysis of Surface Ozone Monitoring Records in Kemaman, Malaysia

Ismail¹,

Ibrahim²,

Ibrahim³

et al. 2012

Air Pollution - A Comprehensive Perspective

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“…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%

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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“…Kaedah ini mempunyai kelebihan tersendiri kerana peramalan sesebuah siri masa, contohnya, ozon adalah dijalankan hanya dengan menggunakan data daripada siri masa ozon sahaja, tanpa melibatkan data dari faktor-faktor lain. Kaedah penghampiran linear setempat telah digunakan oleh Chen et al (1998) untuk meramal siri masa ozon yang dicerap mengikut jam dan Kocak et al (2000) untuk meramal purata harian siri masa ozon. Kedua-dua kajian memperoleh hasil peramalan yang memuaskan.…”

Section: Pengenalanunclassified

Aplikasi Model Baharu Penambahbaikan Pendekatan Kalut ke atas Peramalan Siri Masa Kepekatan Ozon

Hamid

Noorani²

2017

JSM

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ABSTRAK Kajian ini merupakan aplikasi pendekatan kalut ke atas peramalan siri masa bahan pencemar udara ozon di stesen asas Malaysia yang terletak di (2016) melaporkan bahawa pencemaran ozon meningkatkan kadar kematian kerana ia membawa kepada pelbagai penyakit pernafasan dan kardiovaskular. Oleh itu, pembangunan model peramalan ke atas siri masa bahan pencemar udara ozon adalah penting.Sifat sesebuah siri masa boleh dikelaskan kepada berketentuan atau rawak. Siri masa berketentuan adalah siri masa yang boleh diramal manakala siri masa rawak tidak boleh diramal. Sifat kalut berada antara sifat berketentuan dan rawak (Abarbanel 1996). Siri masa kalut boleh diramal; walau bagaimanapun, disebabkan oleh kebergantungan sensitif kepada keadaan awal, maka, bagi sesebuah siri masa kalut, hanya peramalan jangka pendek dibenarkan (Sprott 2003).Terdapat pelbagai pendekatan yang telah digunakan oleh kajian lepas untuk menguji sama ada siri masa ozon adalah kalut atau tidak. Menggunakan kaedah matra korelasi, kamiran kolerasi, entropi dan eksponen Lyapunov, kajian seperti Chattopadhyay dan Chattopadhyay Plot ruang fasa dan kaedah Cao (Cao 1997) dapat mengklasifikasikan sifat siri masa. Walau bagaimanapun, kaedah ini jarang digunakan ke atas siri masa ozon walaupun kedua-dua telah terbukti berkesan oleh kajian

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Nonlinear time series prediction of O3 concentration in Istanbul

Cited by 62 publications

References 13 publications

Time Series Analysis of Surface Ozone Monitoring Records in Kemaman, Malaysia

Time Series Analysis of Surface Ozone Monitoring Records in Kemaman, Malaysia

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

Aplikasi Model Baharu Penambahbaikan Pendekatan Kalut ke atas Peramalan Siri Masa Kepekatan Ozon

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