A novel soft sensor based warning system for hazardous ground-level ozone using advanced damped least squares neural network

Estimation of hazardous air pollutants in the urban environment for maintaining public safety is a significant concern to mankind. In this paper, we have developed an efficient air quality warning system based on a low-cost and robust ground-level ozone soft sensor. The soft sensor was developed based on a novel technique of damped least squares neural network (DLSNN) with greedy backward elimination (GBE) for the estimation of hazardous ground-level ozone. Only three meteorological factors were used as input variables in the estimation of ground-level ozone and we have used weighted k-nearest neighbors (WkNN) classifier with fast response for development of air quality warning system. We have chosen the urban areas of Taiwan for this study and have analyzed seasonal variations in the ground-level ozone concentration of various cities in Taiwan as part of this work. Moreover, descriptive statistics and linear dependence of ozone concentration based on Spearman correlation coefficient, Kendall's tau coefficient, and Pearson coefficient are calculated. The proposed DLSNN/GBE method exhibited excellent performance resulting in very low mean square error (MSE), mean absolute error (MAE), and high coefficient of determination (R-2) compared to other traditional approaches in ozone concentration estimation. We have achieved a good fit in the determination of ozone concentration from meteorological features of atmosphere. Moreover, the excellent performance of proposed urban air quality warning system was evident from the good F1-score value of 0.952 achieved by the WkNN classifier.