Study of LSTM Air Quality Index Prediction Based on Forecasting Timeliness

Abstract

The change of urban air quality is affected by pollutant emission, meteorological conditions and other factors, so air quality prediction is a multi-variable, nonlinear and time-series problem, which is difficult to be predicted by traditional methods. To solve this problem, a circular neural network based on Long Short Time Memory (LSTM) is proposed to predict Air Quality Index (AQI) by considering the pollution sources, meteorological conditions and time series. The transfer entropy is used to select the meteorological factors that affect the strong change of AQI. Combined with the prediction time, the prediction accuracy of this algorithm in the future 0∼48 hours within different forecast time is studied and compared with the traditional BP neural network and the Gated Recurrent Unit (GRU), and then the Root Mean Square Error (RMSE) was used for evaluation. Taking the measured data of hourly air quality index of Chengdu from January 1, 2018 to September 15, 2019 and the measured data of meteorological factors in the same period as experimental examples, the experimental results show that LSTM has better prediction accuracy and robustness than traditional neural networks in the aging of 0∼48h forecasting, and has the advantages of temporary forecasting and short-term forecasting. At the same time, it is verified that GRU has no obvious advantage in air quality index prediction application compared with LSTM.