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

作者：Inal Fikret^*

来源：Clean-Soil Air Water, 2010, 38(10): 897-908.

DOI：10.1002/clen.201000138

• Artificial neural network (ANN)
• Istanbul
• Multi layer perceptron (MLP)
• Ozone
• Regression

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 (d(2)) for the testing data were 0 90 8 78 mu g/m(3) 11 15 mu g/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 PM10 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

• 出版日期2010-10