Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model

Abstract

In the context of global climate change, air quality prediction work has a substantial impact on humans’ daily lives. The current extensive usage of machine learning models for air quality forecasting has resulted in significant improvements to the sector. The long short-term memory network is a deep learning prediction model, which adds a forgetting layer to a recurrent neural network and has several applications in air quality prediction. The experimental data presented in this research include air pollution data (SO2, NO2, PM10, PM2.5, O3, and CO) and meteorological data (temperature, barometric pressure, humidity, and wind speed). Initially, using air pollution data to calculate the air pollution index (AQI) and the wavelet transform with the adaptive Stein risk estimation threshold is utilized to enhance the quality of meteorological data. Using detrended cross-correlation analysis (DCCA), the mutual association between pollution elements and meteorological elements is then quantified. On short, medium, and long scales, the prediction model’s accuracy increases by 1%, 1.6%, 2%, and 5% for window sizes (h) of 24, 48, 168, and 5000, and the efficiency increases by 5.72%, 8.64%, 8.29%, and 3.42%, respectively. The model developed in this paper has a substantial improvement effect, and its application to the forecast of air quality is of immense practical significance.

Keywords

• wavelet transform
• detrended covariance analysis
• long short-term memory network
• sliding window scale