An LSTM-based aggregated model for air pollution forecasting

Abstract

During the past few years, severe air-pollution problem has garnered worldwide attention due to its effect on health and wellbeing of individuals. As a result, the analysis and prediction of air pollution has attracted a good deal of interest among researchers. The research areas include traditional machine learning, neural networks and deep learning. How to effectively and accurately predict air pollution becomes an important issue. In this paper, we propose an Aggregated LSTM (Long Short-Term Memory) model (ALSTM) based on the LSTM deep learning method. In this new model, we combine local air quality monitoring station, the station in nearby industrial areas, and the stations for external pollution sources. To improve prediction accuracy, we aggregate three LSTM models into a predictive model for early predictions based on external sources of pollution and information from nearby industrial air quality stations. We exploited the data with 17 attributes collected by Taiwan Environmental Protection Agency from 2012 to 2017 as the training data to build the ALSTM forecasting model, and we tested the model using the data collected in 2018. We conducted some experiments to compare our new ALSTM model with SVR (Support Vector Machine based Regression), GBTR (Gradient Boosted Tree Regression), LSTM, etc., in the prediction of PM2.5 for 1–8 h, and evaluated them using various assessment techniques, such as MAE, RMSE, and MAPE. The results reveal that the proposed aggregated model can effectively improve the accuracy of prediction.