Accurate Multi-Site Daily-Ahead Multi-Step PM2.5 Concentrations Forecasting Using Space-Shared CNN-LSTM

Accurate multi-step PM2.5 (particulate matter with diameters ≤2.5um) concentration prediction is critical for humankinds’ health and air population management because it could provide strong evidence for decision-making. However, it is very challenging due to its randomness and variability. This paper proposed a novel method based on convolutional neural network (CNN) and long-short-term memory (LSTM) with a space-shared mechanism, named space-shared CNN-LSTM (SCNN-LSTM) for multi-site daily-ahead multi-step PM2.5 forecasting with self-historical series. The proposed SCNN-LSTM contains multi-channel inputs, each channel corresponding to one-site historical PM2.5 concentration series. In which, CNN and LSTM are used to extract each site's rich hidden feature representations in a stack mode. Especially, CNN is to extract the hidden short-time gap PM2.5 concentration patterns; LSTM is to mine the hidden features with long-time dependency. Each channel extracted features are merged as the comprehensive features for future multi-step PM2.5 concentration forecasting. Besides, the space-shared mechanism is implemented by multi-loss functions to achieve space information sharing. Therefore, the final features are the fusion of short-time gap, long-time dependency, and space information, which enables forecasting more accurately. To validate the proposed method's effectiveness, the authors designed, trained, and compared it with various leading methods in terms of RMSE, MAE, MAPE, and R2 on four real-word PM2.5 data sets in Seoul, South Korea. The massive experiments proved that the proposed method could accurately forecast multi-site multi-step PM2.5 concentration only using self-historical PM2.5 concentration time series and running once. Specifically, the proposed method obtained averaged RMSE of 8.05, MAE of 5.04, MAPE of 23.96%, and R2 of 0.7 for four-site daily ahead 10-hour PM2.5 concentration forecasting.