A Deep Recurrent Neural Network For Air Quality Classification

Having attracted attention worldwide,air pollutions are considered to have detrimental effects on human health.Many cities have suffered severe air pollution.However,monitoring the real-time air quality expenses much and the operation process is very difficult.Forecasting performance of air quality,thus,becomes an important issue for the welfare of people.In this research,we attempt to use a deep learning method to predict Air Quality Classification(AQC)on three famous industrial cities in United States.The Recurrent Neural Network(RNN)of deep learning is used to build a major prediction model.RNN can process and memorize the sequential data such as data concerning daily air quality in a given period of time.The experimental results show the performances on three models including Support Vector Machine,Random Forest and RNN.Our proposed RNN model has best results compared with two machine learning approaches in different experiments with different feature sets and data lengths.Using individual air quality indexes of six pollutants as input variables makes accuracy higher compared with those of concentrations.Concentrations of six pollutants combined with air quality index are considered as the most efficient feature sets.In addition,the sequential data on air quality problem used by RNN with memory model outperforms without memory operation for the reason that the dynamic attributes of data are fully taken into consideration.