Air quality prediction at new stations using spatially transferred bi-directional long short-term memory network

In the last decades, air pollution has been a critical environmental issue, especially in developing countries like China. The governments and scholars have spent lots of effort on controlling air pollution and mitigating its impacts on human society. Accurate prediction of air quality can provide essential decision-making supports, and therefore, scholars have proposed various kinds of models and methods for air quality forecastings, such as statistical methods, machine learning methods, and deep learning methods. Deep learning-based networks, such as RNN and LSTM, have been reported to achieve good performance in recent studies. However, the excellent performance of these methods requires sufficient data to train the model. For stations that lack data, such as newly built monitoring stations, the performance of those methods is constrained. Therefore, a methodology that could address the data shortage problem in new stations should be explored. This study proposes a transfer learning-based stacked bidirectional long short term memory (TLS-BLSTM) network to predict air quality for the new stations that lack data. The proposed method integrates advanced deep learning techniques and transfer learning strategies to transfer the knowledge learned from existing air quality stations to new stations to boost forecasting. A case study in Anhui, China, was conducted to evaluate the effectiveness of TLS-BLSTM. The results show that the proposed method can help achieve 35.21% lower RMSE on average for the experimented three pollutants in new stations.