Investigation of performance of tropospheric ozone estimations in the industrial region using differential Artificial Neural Networks methods

Abstract

The method of Levenberg-Marquardt learning algorithm was investigated for estimating tropospheric ozone concentration. The Levenberg-Marquardt learning algorithm has 12 input neurons (6 pollutants and 6 meteorological variables), 28 neurons in the hidden layer, and 1 output neuron for the Ozone (O-3) estimate. The Multilayer Perceptron Model (MLP) performance was found to make good predictions with the mean square error (MSE) less than 1 mu g/m(3) (0.002 mu g/m(3)). In addition, the correlation coefficient ranges from 0.74 to 0.95 in The Levenberg-Marquardt learning. The Levenberg-Marquardt learning algorithm that a multilayer perception method of Artificial Neural Network (ANN) has performed well and an effective approach for predicting tropospheric ozone. Ozone concentration was influenced predominantly by the nitrogen oxide (NOx, NO2, NO), SO2 and temperature. The model did not predict solar radiation to ozone with sufficient accuracy.