Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2648
Title: Artificial neural network prediction of tropospheric ozone concentrations in Istanbul, Turkey
Authors: İnal, Fikret
Keywords: Artificial neural networks
Multi-layer perceptron
Ozone
Regression
Istanbul
Publisher: John Wiley and Sons Inc.
Source: İnal, F. (2010). Artificial neural network prediction of tropospheric ozone concentrations in Istanbul, Turkey. Clean - Soil, Air, Water, 38(10), 897-908. doi:10.1002/clen.201000138
Abstract: Tropospheric (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, cross-validation, 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 (d2) for the testing data were 0.90, 8.78 μg/m3, 11.15μg/m3, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM10, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d2 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. Tropospheric ozone has adverse effects on human health and environment. Here, the next-day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron type artificial neural networks (MLP-ANNs). The MLP-ANNs were compared to multiple linear and multiple non-linear regression models. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
URI: http://doi.org/10.1002/clen.201000138
http://hdl.handle.net/11147/2648
ISSN: 1863-0650
1863-0650
Appears in Collections:Chemical Engineering / Kimya Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Sürdürülebilir Yeşil Kampüs Koleksiyonu / Sustainable Green Campus Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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