Ozone is one of the most effective pollutants in lower atmosphere. Concentration of ozone in atmosphere reveals its impact on plants, human and on other organic materials. Many techniques had been used in past to calculate the concentration of ozone with the help of other environmental factors like wind, humidity, temperature and etc. Prediction models like Artificial Neural Network (ANN) have gained much reputation in calculating accurate results with learning data. This paper shows a study of integration of predicted ozone concentration by two ANN proposed models. The study initiated with data collection from the study area. The collected data is then fed to the proposed ANN models as training data to get the concentrations of ozone with many input variables temperature, humidity, wind speed, incoming solar radiation, sulfur dioxide, nitrogen dioxide, and previous ozone data as predictor. The study shows the great dependence of ozone concentration upon environmental factors. The two proposed Back Propagation (BP) models clearly gave good results according to statistical indicators. In terms of the gradient, mean error and the standard deviation values, the proposed two BP models perform well for both data sets.
Similarly, REF have examined two NN models including Feed-forward NN and Back-propagation NN in terms of ozone prediction.
Prediction of Maximum Ground Ozone Levels using Neural Network
International Journal of Computing and Digital Systems
Environmental Science