Air quality prediction method based on improved wavelet denoising and LSTM

Abstract

in order to optimize the traditional wavelet de-noising algorithm threshold function in continuity and noise reduction effect, an improved soft hard threshold compromise method and deep learning model are proposed to analyze the prediction performance of different deep learning models and improved wavelet algorithm. RNN, GRU and LSTM neural networks are constructed to predict the original data, the traditional wavelet denoising data and the improved wavelet denoising data. The air quality data of Chengdu were used for simulation experiment. The results of quantitative analysis showed that: for AQI, PM2.5 and O3_ Oh denoising will cause the loss of useful signals. The improved wavelet threshold denoising for PM10, SO2, Co, NO2 data can improve the prediction effect of the model. The results show that LSTM model has good applicability in air quality prediction, the absolute percent error is 5.867%, and the mean square error is 4.870. The prediction performance of LSTM model is about 10%, the absolute percent error is 5.176%, and the mean square error is 5.314. The traditional threshold denoising will affect the prediction results of the model for the actual data due to the loss of high-frequency signal and pseudo Gibbs phenomenon. The results show that the improved soft hard threshold compromise method can better deal with air quality data, and the LSTM model based on improved wavelet denoising has strong applicability for air quality prediction.