Prediction and examination of seasonal variation of ozone with meteorological parameter through artificial neural network at NEERI, Nagpur, India

- This study proposes ozone prediction by using artificial neural network.
- RBF, GRNN and MLP models of ANN were assessed and MLP was found best-fitted model.
- The result shows seasonal variation of ozone with NO2 and meteorology.

The present study focused on seasonal relations and predictions of the ozone (O3) coupled with NO2 and meteorology. Monitoring of ozone concentration throughout year shows an increasing trend during summer and a decreasing trend in the winter season. A comparison between three types of ANN; multilayer perceptron trained (MLP) with back-propagation, radial basis functions (RBF) and generalized regression neural network (GRNN) for short prediction of ozone are conclusively demonstrated. The model results are validated with observations from next monsoon. Based on the model's performance, the MLP back propagation model gives the best correlation between observed and predicted ozone concentrations than other models. Performance assessment parameters considered in the study also indicates that MLP is the best-fit model for prediction of ozone concentration throughout the year.