Flight delay is inevitable and it plays an important role in both profits and loss of the airlines. An accurate estimation of flight delay is critical for airlines because the results can be applied to increase customer satisfaction and incomes of airline agencies. There have been many researches on modeling and predicting flight delays, where most of them have been trying to predict the delay through extracting important characteristics and most related features. However, most of the proposed methods are not accurate enough because of massive volume data, dependencies and extreme number of parameters. This paper proposes a model for predicting flight delay based on Deep Learning (DL). DL is one of the newest methods employed in solving problems with high level of complexity and massive amount of data. Moreover, DL is capable to automatically extract the important features from data. Furthermore, due to the fact that most of flight delay data are noisy, a technique based on stack denoising autoencoder is designed and added to the proposed model. Also, Levenberg-Marquart algorithm is applied to find weight and bias proper values, and finally the output has been optimized to produce high accurate results. In order to study effect of stack denoising autoencoder and LM algorithm on the model structure, two other structures are also designed. First structure is based on autoencoder and LM algorithm (SAE-LM), and the second structure is based on denoising autoencoder only (SDA). To investigate the three models, we apply the proposed model on U.S flight dataset that it is imbalanced dataset. In order to create balance dataset, undersampling method are used. We measured precision, accuracy, sensitivity, recall and F-measure of the three models on two cases. Accuracy of the proposed prediction model analyzed and compared to previous prediction method. results of three models on both imbalanced and balanced datasets shows that precision, accuracy, sensitivity, recall and F-measure of SDA-LM model with imbalanced and balanced dataset is improvement than SAE-LM and SDA models. The results also show that accuracy of the proposed model in forecasting flight delay on imbalanced and balanced dataset respectively has greater than previous model called RNN.

航班延误是不可避免的，在航空公司的盈利和亏损中都起着重要作用。准确估计航班延误对航空公司至关重要，因为可以将结果应用于增加客户满意度和航空公司的收入。在建模和预测航班延误方面已有许多研究，其中大多数研究都试图通过提取重要特征和大多数相关特征来预测延误。但是，由于海量数据，依赖性和大量参数，大多数建议的方法不够精确。本文提出了一种基于深度学习（DL）的航班延误预测模型。DL是用于解决具有高度复杂性和大量数据的问题的最新方法之一。此外，DL能够自动从数据中提取重要特征。此外，由于大多数飞行延迟数据都是有噪声的事实，因此设计了一种基于堆栈去噪自动编码器的技术并将其添加到所提出的模型中。此外，应用Levenberg-Marquart算法查找权重并偏置适当的值，最后对输出进行了优化以产生高精度的结果。为了研究堆栈去噪自动编码器和LM算法对模型结构的影响，还设计了另外两种结构。第一种结构基于自动编码器和LM算法（SAE-LM），第二种结构仅基于降噪自动编码器（SDA）。为了研究这三个模型，我们将提出的模型应用于美国航班数据集，因为它是不平衡数据集。为了创建余额数据集，使用了欠采样方法。我们在两种情况下测量了这三个模型的精度，准确性，灵敏度，召回率和F度量。对所提出的预测模型的准确性进行了分析，并与先前的预测方法进行了比较。三种模型在不平衡和平衡数据集上的结果表明，具有不平衡和平衡数据集的SDA-LM模型的精度，准确性，灵敏度，召回率和F度量比SAE-LM和SDA模型有所提高。结果还表明，所提出的模型分别在不平衡和平衡数据集上预测航班延误的准确性要高于先前称为RNN的模型。三种模型在不平衡和平衡数据集上的结果表明，具有不平衡和平衡数据集的SDA-LM模型的精度，准确性，灵敏度，召回率和F度量比SAE-LM和SDA模型有所提高。结果还表明，所提出的模型分别在不平衡和平衡数据集上预测航班延误的准确性要高于先前称为RNN的模型。三种模型在不平衡和平衡数据集上的结果表明，具有不平衡和平衡数据集的SDA-LM模型的精度，准确性，灵敏度，召回率和F度量比SAE-LM和SDA模型有所提高。结果还表明，所提出的模型分别在不平衡和平衡数据集上预测航班延误的准确性要高于先前称为RNN的模型。