Nowadays, the civil aviation industry has a high precision demand of flight delay prediction. To make full use of the characteristics of flight data and meteorological data, two flight delay prediction models using deep convolution neural network based on fusion of meteorological data are proposed in this paper. One is DCNN (Dual-channel Convolutional Neural Network), which refers to the ResNet network structure. The other is SE-DenseNet (Squeeze and Excitation-Densely Connected Convolutional Network), combining the advantages of DenseNet and SENet. Firstly, flight data and meteorological data are fused in the model. Then, both DCNN and SE-DenseNet models are used to extract feature automatically based on the fused flight data set. Finally, the softmax classifier is adopted to predict the flight delay level. For proposed DCNN model, both straight channel and convolution channel are designed to guarantee the lossless transmission of the feature matrix and enhance the patency of the deep network. For proposed SE-DenseNet model, a SE module is added after the convolution layer of each DenseNet block, which can not only enhance the transmission of deep information but also achieve feature recalibration in the feature extraction process. The research results indicate that after considering characteristics of meteorological information, the accuracy of the model can be improved 1% compared with only considering the flight information. The two deep convolutional neural networks proposed in this paper, DCNN and SE-DenseNet, can both effectively improve the prediction accuracies, reaching to 92.1% and 93.19%, respectively.

如今，民航业对航班延误预测有很高的要求。为了充分利用飞行数据和气象数据的特点，提出了两种基于气象数据融合的深度卷积神经网络的飞行延误预测模型。一种是DCNN（双通道卷积神经网络），它指的是ResNet网络结构。另一个是SE-DenseNet（压缩和激励-密集连接的卷积网络），结合了DenseNet和SENet的优势。首先，将飞行数据和气象数据融合到模型中。然后，DCNN和SE-DenseNet模型均用于基于融合的飞行数据集自动提取特征。最后，采用softmax分类器来预测飞行延迟水平。对于建议的DCNN模型，直通道和卷积通道均旨在确保特征矩阵的无损传输，并增强深度网络的通畅性。对于提出的SE-DenseNet模型，在每个DenseNet块的卷积层之后添加SE模块，这不仅可以增强深度信息的传输，而且可以在特征提取过程中实现特征重新校准。研究结果表明，考虑气象信息的特征，与仅考虑飞行信息相比，该模型的精度可以提高1％。本文提出的两个深度卷积神经网络DCNN和SE-DenseNet都可以有效地提高预测精度，分别达到92.1％和93.19％。