The application of strategy based on LSTM for the short-term prediction of PM2.5 in city

Many cities have long suffered from the events of fine particulate matter (PM2.5) pollutions. The Taiwanese Government has long strived to accurately predict the short-term hourly concentration of PM2.5 for the warnings on air pollution. Long Short-Term Memory neural network (LSTM) based on deep learning improves the prediction accuracy of daily PM2.5 concentration but PM2.5 prediction for next hours still needs to be improved. Therefore, this study proposes innovative Application-Strategy-based LSTM (ASLSTM) to accurately predict the short-term hourly PM2.5 concentrations, especially for the high PM2.5 predictions. First, this study identified better spatiotemporal input feature of a LSTM for obtaining this Better LSTM (BLSTM). In doing so, BLSTM trained by appropriate datasets could accurately predict the next hourly pollution concentration. Next, the application strategy was applied on BLSTM to construct ASLSTM. Specifically, from a timeline perspective, ASLSTM concatenates several BLSTMs to predict the concentration of PM2.5 at the following next several hours during which the predicted outputs of BLSTM at this time t was selected and included as the inputs of the next BLSTM at the next time t + 1, and the oldest input used as BLSTM at the time t was removed. The result demonstrated that BLSTM were trained by the dataset collected from 2008 to 2010 at Dali measurement station because there is a relatively large amount of data on high PM2.5 concentration in this dataset. Besides, a comparison of the performance of the ASLSTM with that of the LSTM was made to validate this proposed ASLSTM, especially for the range of higher PM2.5 concentration that people concerned. More importantly, the feasibility of this proposed application strategy and the necessity of optimizing the input parameters of LSTM were validated. In summary, this ASLSTM could accurately predict the short-term PM2.5 in Taichung city.