A Hybrid Air Quality Index Prediction Model Based on CNN and Attention Gate Unit

Abstract

Air Quality Index (AQI) is the crucial foundation for measuring air quality, which reflects the influence of air quality on people’s health and life to a certain extent. In this paper, a hybrid AQI prediction model based on Convolutional Neural Network (CNN) and Attention Gate Unit (AGU) is proposed to deal with the problems of the “vanishing gradient” and “exploding gradient” of Recurrent Neural Network (RNN). AGU is a new model proposed in this paper to embed the attention mechanism and Data Adjustment Module (DAM) into the gated unit. The attention mechanism enhances the learning ability of the gated unit, and the DAM makes the gated unit more sensitive to historical data learning. In this model, CNN plays a role in extracting features from time series data. AGU can make differentiated learning of historical data and finally produce prediction results. The model evaluation indexes used in the experiments are Mean Absolute Error (MAE), Mean Square Error (MSE), and R Squared (R2). The experimental results show that the overall performance of the AQI prediction model based on CNN-AGU is superior to that of other models by comparing with the other nine models on the same data set.