基于深度全连接神经网络的离港航班延误预测模型

针对提升离港航班延误预测精确度困难的问题，提出一种基于深度全连接神经网络（DFCNN）的离港航班延误预测模型。首先，在考虑航班信息、机场气象与航班延误历史的基础上，考虑航班网络结构对预测模型的影响；然后，从激活函数、输入数据项及延误时间阈值三个维度进行实验，以对模型抑制梯度弥散与提升学习表现能力的能力进行了优化与验证；最后，通过调控神经网络层数的纵向拓展方式与随机丢失层的Dropout参数，提升模型的泛化能力。实验结果表明：所提模型使用tanh、指数线性函数（ELU），预测精确度比使用线性整流函数（ReLU）分别提升了1.26、1.28个百分点；考虑航班网络结构后，所提模型采用ELU函数计算时，预测精确度比未考虑航班网络结构时提升了3.12个百分点；在时间阈值为60 min时，通过调控Dropout参数，模型的损失值不断降低；在5层隐含层网络和Dropout参数为0.3时，所提模型可以取得92.39%的预测精确度。因此，所提模型能够对国内航班延误做出较为准确的判断。

Departure flight delay prediction model based on deep fully connected neural network

Aiming at the problem that it is difficult to improve the accuracy of departure flight delay prediction， a departure flight delay prediction model based on Deep Fully Connected Neural Network （DFCNN） was proposed. Firstly， on the basis of considering flight information， airport weather and flight delay history， the influence of flight network structure on prediction model was considered. Secondly， experiments were carried out from three dimensions of activation function， input data item and delay time threshold to optimize and verify the model ability to suppress gradient dispersion and improve the learning performance. Finally， through adjusting the vertical expansion method of the number of neural network layers and the Dropout parameters of the random loss layers， the generalization ability of the model was improved. The results of experiments indicate that the prediction accuracy of the proposed model can be improved by 1.26 percentage points and 1.28 percentage points respectively after using tanh and Exponential Linear Unit （ELU） functions in the proposed model than using Rectified Linear Unit （ReLU）. After considering the flight network structure， the prediction accuracy calculated by the proposed model using ELU function is improved by 3.12 percentage points than without considering the flight network structure. When the Dropout parameters are adjusted， the loss value of the model is continuously reduced with 60 min time threshold. With a 5-layer hidden layer network and a Dropout parameter of 0.3， the prediction accuracy of 92.39% can be achieved by the proposed model. Therefore， the proposed model can make more accurate judgments on domestic flight delays.