Constructing a PM2.5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks

Air pollution problems have a severe effect on the natural environment and public health. The application of machine learning to air pollutant data can result in a better understanding of environmental quality. Of these methods, the deep learning method has proven to be a very efficient and accurate method to forecast complex air quality data. This paper proposes a deep learning model based on an auto-encoder and bidirectional long short-term memory (Bi-LSTM) to forecast PM2.5 concentrations to reveal the correlation between PM2.5 and multiple climate variables. The model comprises several aspects, including data preprocessing, auto-encoder layer, and Bi-LSTM layer. The performance of the proposed model was verified based on a real-world air pollution dataset, and the results indicated this model can improve the prediction accuracy in an experimental scenario. •The deep learning neural network is introduced in constructing prediction model for PM2.5 concentration.•The proposed model is based on combining auto-encoder with Bi-LSTM neural networks, namely AE-Bi-LSTM model.•The Auto-encoder layer of the proposed model aims to extract the internal features of pollution data.•The Bi-LSTM layer of the proposed model aims to predict the PM2.5 concentration as a time series problem.