Flight delay prediction based on deep learning and Levenberg-Marquart algorithm

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...

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Vydáno v:Journal of big data Ročník 7; číslo 1; s. 1 - 28
Hlavní autoři: Yazdi, Maryam Farshchian, Kamel, Seyed Reza, Chabok, Seyyed Javad Mahdavi, Kheirabadi, Maryam
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 26.11.2020
Springer Nature B.V
SpringerOpen
Témata:
Accuracy
Air travel
Airlines
Algorithms
Big Data
Client satisfaction
Communications Engineering
Computational Science and Engineering
Computer Science
Customer satisfaction
Data
Data Mining and Knowledge Discovery
Database Management
Datasets
Deep learning
Delay
Denoising
Feature extraction
Flight delay prediction
Forecasting
Information Storage and Retrieval
Learning
Machine learning
Mathematical Applications in Computer Science
Model accuracy
Networks
Noise reduction
Prediction models
Predictions
Profits
Recall
Satisfaction
Sensitivity
Stacked denoising autoencoders
Deep learning
Big data
Flight delay prediction
Stacked denoising autoencoders
ISSN:2196-1115, 2196-1115
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Abstract 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.
AbstractList 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.
Abstract 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.
ArticleNumber 106
Author Kamel, Seyed Reza
Kheirabadi, Maryam
Yazdi, Maryam Farshchian
Chabok, Seyyed Javad Mahdavi
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  givenname: Seyyed Javad Mahdavi
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  fullname: Chabok, Seyyed Javad Mahdavi
  organization: Department of Computer Engineering, Mashhad Branch, Islamic Azad University
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Keywords Deep learning
Big data
Flight delay prediction
Stacked denoising autoencoders
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Snippet 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...
Abstract 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...
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SubjectTerms Accuracy
Air travel
Airlines
Algorithms
Big Data
Client satisfaction
Communications Engineering
Computational Science and Engineering
Computer Science
Customer satisfaction
Data
Data Mining and Knowledge Discovery
Database Management
Datasets
Deep learning
Delay
Denoising
Feature extraction
Flight delay prediction
Forecasting
Information Storage and Retrieval
Learning
Machine learning
Mathematical Applications in Computer Science
Model accuracy
Networks
Noise reduction
Prediction models
Predictions
Profits
Recall
Satisfaction
Sensitivity
Stacked denoising autoencoders
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Title Flight delay prediction based on deep learning and Levenberg-Marquart algorithm
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