Air quality forecast is an important technical means to ensure timely and proper response to heavy pollution weather. In this study, a hybrid deep air quality predictor (HDAQP) model consisting of one-dimensional convolutional neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN) is proposed to forecast air quality indicators (mainly PM2.5 concentrations). The proposed model can overcome the limitations of the single model and meanwhile make the best of each. CNN model is used to convolve the historical PM2.5 concentration data along with meteorological data to extract shallow features, while LSTM model is used to extract the deep temporal features. Finally, the DNN model is adopted to transfer these deep features into the final forecast results. Compared with the mainstream deep learning models (e.g., RNN, LSTM, and CNN-LSTM models), the HDAQP model exhibits a better performance in short-term PM2.5 concentration forecast. With the increase of prediction time, the long-term prediction performance of the HDAQP model will be degraded, but it is still better than the mainstream deep learning models. Moreover, considering other meteorological factors, with the multi-source data, the HDAQP model can forecast PM2.5 concentrations more accurately.

中文翻译：

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一种用于 PM2.5 浓度预测的多源数据混合深度学习模型

空气质量预报是确保及时、妥善应对重污染天气的重要技术手段。在本研究中，提出了一种由一维卷积神经网络 (CNN)、长短期记忆 (LSTM) 和深度神经网络 (DNN) 组成的混合深度空气质量预测器 (HDAQP) 模型来预测空气质量指标。主要是 PM2.5 浓度）。所提出的模型可以克服单一模型的局限性，同时充分利用每个模型。CNN模型用于将历史PM2.5浓度数据与气象数据卷积以提取浅层特征，而LSTM模型用于提取深层时间特征。最后，采用 DNN 模型将这些深层特征转化为最终的预测结果。与主流的深度学习模型（例如，RNN、LSTM、和 CNN-LSTM 模型），HDAQP 模型在短期 PM2.5 浓度预测中表现出更好的性能。随着预测时间的增加，HDAQP模型的长期预测性能会有所下降，但仍优于主流的深度学习模型。此外，考虑到其他气象因素，HDAQP模型可以通过多源数据更准确地预测PM2.5浓度。