Prediction of the Flight Delay Based on Neural Network and Support Vector Machine: A Comparative Study

Yuxin Huang

doi:10.47297/taposatWSP2633-456910.20230402

PDF
导出
分享
收藏
专辑

Prediction of the Flight Delay Based on Neural Network and Support Vector Machine: A Comparative Study

4卷第2期

浏览量: 0 Downloads: 0

Prediction of the Flight Delay Based on Neural Network and Support Vector Machine: A Comparative Study
Prediction of the Flight Delay Based on Neural Network and Support Vector Machine: A Comparative Study
Tan Kah Kee College of Xiamen University,Xiamen,China,363105
2023年4卷第2期页码：68-72
纸质出版日期： 2023 ，
DOI：10.47297/taposatWSP2633-456910.20230402
Accepted：

Scan QR Code

(2023). Prediction of the Flight Delay Based on Neural Network and Support Vector Machine: A Comparative Study. 科技理论与实践（英文版）, 4(2), 68-72.

YUXIN HUANG. (2023). Prediction of the Flight Delay Based on Neural Network and Support Vector Machine: A Comparative Study. Theory and Practice of Science and Technology, 4(2), 68-72..
(2023). Prediction of the Flight Delay Based on Neural Network and Support Vector Machine: A Comparative Study. 科技理论与实践（英文版）, 4(2), 68-72. DOI： 10.47297/taposatWSP2633-456910.20230402.

YUXIN HUANG. (2023). Prediction of the Flight Delay Based on Neural Network and Support Vector Machine: A Comparative Study. Theory and Practice of Science and Technology, 4(2), 68-72.. DOI： 10.47297/taposatWSP2633-456910.20230402.

摘要

Abstract

This paper training a model which aims to predict the flight’s delay based on the ANN and SVM

which are current mainstream machine learning models. We use the dataset of the flight delay from Kaggle and divided it as three parts: 0.7:0.15:0.15

for training dataset

validation and test dataset. The mean relative error of the prediction model are 18.6% and 21.2% by ANN and SVM

and the time cost of predicting a single roughness value based on cutting parameters are about 100.4ms and 58.5ms with ANN and SVM. Based on the experimental results

these two models can accurately and efficiently predict the delay and the ANN model have a little better accuracy than the SVM.

关键词

Keywords

Flight delayNeural networkSupport vector machine

references

Moreira, L., Dantas, C., Oliveira, L., et al. (2018) On evaluating data preprocessing methods for machine learning models for flight delays. In: IJCNN. Rio de Janeiro. pp. 1-8.[2] Zhang, S., Li X., Zong M., et al. (2017) Efficient kNN classification with different numbers of nearest neighbors. IEEE transactions on neural networks and learning systems, 29(5): 1774-85.[3] Sun, J., Wu, Z., Yin, Z., et al. (2018) SVM-CNN-Based fusion algorithm for vehicle navigation considering atypical observations. IEEE signal processing letters, 26(2): 212-16.[4] Pamplona, D.A., Li, W., Barros, A.G., et al. (2018) Supervised neural network with multilevel input layers for predicting of air traffic delays. In: IJCNN. Rio de Janeiro. pp. 1-6.[5] Indolia, S., Goswami, A.K., Mishra, S.P., et al. (2018) Conceptual understanding of convolutional neural network-a deep learning approach. Procedia computer science, 132: 679-88.

文章被引用时，请邮件提醒。

Submit

Two Multi-Unit Artificial Intelligence Decision Modes in Video Games：A Comparison

A CNN-based Physical Layer Design for Communications System

Related Author

Qisen Lin

Xinyi Li

Related Institution

School of Information Engineering, Wuhan University of Technology

University of Electronic Science and Technology of China (UESTC)University of Glasgow

⁰