Prediction of PM2.5 Concentration Based on CNN-BiGRU Model

Abstract

The issue of air pollution has always been a concern. Bad smog weather not only brings inconvenience to people's travel, but also poses a threat to people's health. PM2.5 concentration is an important indicator of air conditions. Therefore, it is of long-term significance to analyze and predict the concentration of PM2.5. Aiming at the problem that a single machine learning model cannot consider the impact of multiple factors on PM2.5 concentration changes, and the data characteristics are complex, which cannot better capture all the characteristics of the data, and cannot highlight the regularity of PM2.5 changes over time, the construction of a combined model further improves the prediction accuracy. Firstly, based on the PM2.5 concentration values, air quality data, and meteorological data at various stations in New Taipei City, Taiwan Province, through analyzing the spatiotemporal distribution characteristics of the PM2.5 concentration at the target station, as well as the correlation with various pollutant factors and meteorological factors, Spearman correlation analysis is used for feature selection. The combined model CNN-BiGRU constructed in this paper utilizes its unique convolution operation to extract features from one-dimensional data, and combines the circular neural network BiGRU with bidirectional transmission function to model and predict PM2.5 concentration based on the functional advantages of both parties.