Improved gray correlation analysis and combined prediction model for aviation accidents
ISSN: 0264-4401
Article publication date: 9 August 2023
Issue publication date: 12 October 2023
Abstract
Purpose
The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.
Design/methodology/approach
This paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.
Findings
The global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.
Originality/value
Unlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.
Keywords
Acknowledgements
Since acceptance of this article, the following author(s) have updated their affiliation: Yuanyuan Guo is at the School of Civil Aviation, Zhengzhou University of Aeronautics, Zhengzhou, China.
This work was funded by the Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China (Nos: U2033202 and U1333119) and the National Natural Science Foundation of China (No: 52172387).
Citation
Su, S., Sun, Y., Peng, C. and Guo, Y. (2023), "Improved gray correlation analysis and combined prediction model for aviation accidents", Engineering Computations, Vol. 40 No. 7/8, pp. 1570-1592. https://doi.org/10.1108/EC-06-2022-0384
Publisher
:Emerald Publishing Limited
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