Recurrent Learning on PM2.5 Prediction Based on Clustered Airbox Dataset

Abstract

The progress of industrial development naturally leads to the demand for more electrical power. Unfortunately, due to the fear of the safety of nuclear power plants, many countries have relied on thermal power plants, which will cause more air pollutants during the process of coal burning. This phenomenon as well as increased vehicle emissions around us, have constituted the primary factors of serious air pollution. Inhaling too much particulate air pollution may lead to respiratory diseases and even death, especially PM <inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> . By predicting the air pollutant concentration, people can take precautions to avoid overexposure to air pollutants. Consequently, accurate PM <inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> prediction becomes more important. In this study, we propose a PM <inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> prediction system, which utilizes the dataset from EdiGreen Airbox and Taiwan EPA. Autoencoder and Linear interpolation are adopted for solving the missing value problem. Spearman’s correlation coefficient is used to identify the most relevant features for PM <inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> . Two prediction models (i.e., LSTM and LSTM based on K-means) are implemented which predict PM <inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> value for each Airbox device. To assess the performance of the model prediction, the daily average error and the hourly average accuracy for the duration of a week are calculated. The experimental results show that LSTM based on K-means has the best performance among all methods. Therefore, LSTM based on K-means is chosen to provide real-time PM <inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> prediction through the Linebot.