Hybrid model of Air Quality Prediction Using K-Means Clustering and Deep Neural Network

With the rapid development of economy and the emission of a lot of polluted gases, air pollution is increasingly serious. Air quality prediction is an effective way to provide early warning of harmful air pollutants, which can protect public health. A hybrid model of air quality prediction which uses K-Means clustering and deep neural network is proposed in this paper. The deep neural network with capacity of regressive computation consists of bidirectional LSTM (Long Short-Term Memory) and fully connected neural network. First of all, the historical meteorological monitoring data of Qingdao City is taken as the research target, and the meteorological data is divided into four categories according to the quarter by k-Means clustering algorithm. Then the classified meteorological data and the data of historical concentration of air pollutants are used to train neural network. A better hyperparameter combination is selected by lots of trial. Next, the hybrid model is applied on the test set, and the mean square error between predicted value and true value is used as the evaluation criterion of predictive property. Last, through comparing with other algorithm models, it is proved that the proposed hybrid model can achieve higher precision for air quality prediction.