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 PM_2.5 concentrations to reveal the correlation between PM_2.5 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.

空气污染问题严重影响了自然环境和公众健康。将机器学习应用于空气污染物数据可以更好地了解环境质量。在这些方法中，深度学习方法已被证明是预测复杂空气质量数据的非常有效和准确的方法。本文提出了一种基于自动编码器和双向长期短期记忆（Bi-LSTM）的深度学习模型，以预测PM _2.5浓度以揭示PM _2.5之间的相关性以及多个气候变量 该模型包括多个方面，包括数据预处理，自动编码器层和Bi-LSTM层。基于真实世界的空气污染数据集验证了该模型的性能，结果表明该模型可以提高实验场景的预测精度。