A comparative study of the feed forward back propagation (FFBP) and layer recurrent (LR) neural network model for forecasting ground level ozone concentration

Abstract

Ozone (O3) in the troposphere is considered as a secondary air pollutant and has an adverse impact on human health and climatic condition. In many countries including India, O3 is listed as one of the criteria pollutants. Thus, a proper forecasting technique of ozone concentration is necessary for protecting the human health. The concentration of ozone in the troposphere depends on the meteorological condition and precursor’s levels. Hence, it is essential to consider these dependent factors in the development of prediction model. The study aims to develop an ozone forecasting model using artificial neural network (ANN). Three-year air pollution and meteorological data (1 January 2009 to 31 December 2011) of Kolkata City was used for model development. Two types of learning algorithms [feed forward back propagation (FFBP) and layer recurrent (LR)] were used for training the ANN model. Four meteorological factors (relative humidity, temperature, wind speed, and wind direction) along with the NO2 concentration and previous day’s ozone concentration were used as input parameters in the model for predicting the ozone concentration. The number of neurons in the hidden layers of a neural network model was optimized for both the algorithms. The number of input combinations was also optimized using forward search algorithm. The model performances were tested using four statistical indices [percentage of root mean square error (RMSE), coefficient of determination (R 2), fractional bias (FB), index of agreement (IOA)] for evaluating the ANN models.