Air Quality Index Prediction Based on a Long Short-Term Memory Artificial Neural Network Model

Abstract

Air pollution has become one of the important challenges restricting the sustainable development of cities. Therefore, it is of great significance to achieve accurate prediction of Air Quality Index (AQI). Long Short Term Memory (LSTM) is a deep learning method suitable for learning time series data. Considering its superiority in processing time series data, this study established an LSTM forecasting model suitable for air quality index forecasting. First, we focus on optimizing the feature metrics of the model input through Information Gain (IG). Second, the prediction results of the LSTM model are compared with other machine learning models. At the same time the time step aspect of the LSTM model is used with selective experiments to ensure that model validation works properly. The results show that compared with other machine learning models, the LSTM model constructed in this paper is more suitable for the prediction of air quality index.