Modeling and application for air quality evaluation based on improved grey clustering and neural networks classification
Liu, Jie
Wang, Xiaodong
Huang, Ping
Download PDF

How to Cite

Liu J., Wang X., Huang P., 2018, Modeling and application for air quality evaluation based on improved grey clustering and neural networks classification, Chemical Engineering Transactions, 71, 37-42.
Download PDF


According to the air quality monitoring date of the six urban evaluation sites arranged in six main administrative districts in Beijing during the four seasons from 2013 to 2014, an evaluation system based on air quality indexes of Chinese was formulated. And, next, based on concentration limits of six main pollutants including PM10, PM2.5, O3, CO, SO2 and NO2, analysis of the improved grey clustering method and the neural networks classification method were used for evaluation by using the detailed evaluation procedures. An improved grey clustering method based on the whitening function of the exponential types was adopted to improve the calculation precision of the fuzzy factors. It could be seen that the method adopted could more fully reflect the comprehensive effects of all the pollutants on the air quality. Meanwhile, two classification methods based on neural networks were adopted to classify the pollutants into different levels by MATLAB tools. By these methods the comprehensive influence of the pollutants on ambient air could be determined accurately and quickly. The evaluation results demonstrated that the improved grey clustering method which modified weight and extend the whitening function scope could greatly improve information utilization. By considering the weights of whitening values and standard values in different grey classes, the cluster result was more comparable. The neural networks classification method effectively improved the network generalization by the massive data of training samples and testing samples constructed by LINSPACE function in MATLAB tools. The evaluation results obtained by BP and RBF neural network learning showed high accuracy. Among them, the evaluation results of RBF neural network classification and grey clustering modified were more similar that the air quality level of six main administrative districts in Beijing was generally in grade 2. According to the evaluation purposes of the ambient air quality and the practical evaluation demands, it was possible to combine these two methods from different perspectives to gain a more reasonable evaluation result. Thus, it could be concluded that the evaluation of the pollutant contents in the air was of great practical value for eventual heightening of the new ambient air quality standard.
Download PDF