Recurrent Learning on PM2.5 Prediction Based on Clustered Airbox Dataset (Extended Abstract)

By predicting the air pollutant concentration, people can take precautions to avoid overexposure to air pollutants. Consequently, accurate PM2.5 prediction becomes more important. In this paper, we propose a PM2.5 prediction system, which utilizes the dataset from EdiGreen Airbox and Taiwan EPA. Our PM2.5 prediction system is composed of four parts: data collection, data preprocessing, prediction model construction, and Line platform. 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 realtime PM2.5 prediction through the Linebot.