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Research on Aviation Safety Prediction Based on Variable Selection and LSTM
Accurate prediction of aviation safety levels is significant for the efficient early warning and prevention of incidents. However, the causal mechanism and temporal character of aviation accidents are complex and not fully understood, which increases the operation cost of accurate aviation safety pr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823347/ https://www.ncbi.nlm.nih.gov/pubmed/36616640 http://dx.doi.org/10.3390/s23010041 |
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author | Zeng, Hang Guo, Jiansheng Zhang, Hongmei Ren, Bo Wu, Jiangnan |
author_facet | Zeng, Hang Guo, Jiansheng Zhang, Hongmei Ren, Bo Wu, Jiangnan |
author_sort | Zeng, Hang |
collection | PubMed |
description | Accurate prediction of aviation safety levels is significant for the efficient early warning and prevention of incidents. However, the causal mechanism and temporal character of aviation accidents are complex and not fully understood, which increases the operation cost of accurate aviation safety prediction. This paper adopts an innovative statistical method involving a least absolute shrinkage and selection operator (LASSO) and long short-term memory (LSTM). We compiled and calculated 138 monthly aviation insecure events collected from the Aviation Safety Reporting System (ASRS) and took minor accidents as the predictor. Firstly, this paper introduced the group variables and the weight matrix into LASSO to realize the adaptive variable selection. Furthermore, it took the selected variable into multistep stacked LSTM (MSSLSTM) to predict the monthly accidents in 2020. Finally, the proposed method was compared with multiple existing variable selection and prediction methods. The results demonstrate that the RMSE (root mean square error) of the MSSLSTM is reduced by 41.98%, compared with the original model; on the other hand, the key variable selected by the adaptive spare group lasso (ADSGL) can reduce the elapsed time by 42.67% (13 s). This shows that aviation safety prediction based on ADSGL and MSSLSTM can improve the prediction efficiency of the model while keeping excellent generalization ability and robustness. |
format | Online Article Text |
id | pubmed-9823347 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98233472023-01-08 Research on Aviation Safety Prediction Based on Variable Selection and LSTM Zeng, Hang Guo, Jiansheng Zhang, Hongmei Ren, Bo Wu, Jiangnan Sensors (Basel) Article Accurate prediction of aviation safety levels is significant for the efficient early warning and prevention of incidents. However, the causal mechanism and temporal character of aviation accidents are complex and not fully understood, which increases the operation cost of accurate aviation safety prediction. This paper adopts an innovative statistical method involving a least absolute shrinkage and selection operator (LASSO) and long short-term memory (LSTM). We compiled and calculated 138 monthly aviation insecure events collected from the Aviation Safety Reporting System (ASRS) and took minor accidents as the predictor. Firstly, this paper introduced the group variables and the weight matrix into LASSO to realize the adaptive variable selection. Furthermore, it took the selected variable into multistep stacked LSTM (MSSLSTM) to predict the monthly accidents in 2020. Finally, the proposed method was compared with multiple existing variable selection and prediction methods. The results demonstrate that the RMSE (root mean square error) of the MSSLSTM is reduced by 41.98%, compared with the original model; on the other hand, the key variable selected by the adaptive spare group lasso (ADSGL) can reduce the elapsed time by 42.67% (13 s). This shows that aviation safety prediction based on ADSGL and MSSLSTM can improve the prediction efficiency of the model while keeping excellent generalization ability and robustness. MDPI 2022-12-21 /pmc/articles/PMC9823347/ /pubmed/36616640 http://dx.doi.org/10.3390/s23010041 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zeng, Hang Guo, Jiansheng Zhang, Hongmei Ren, Bo Wu, Jiangnan Research on Aviation Safety Prediction Based on Variable Selection and LSTM |
title | Research on Aviation Safety Prediction Based on Variable Selection and LSTM |
title_full | Research on Aviation Safety Prediction Based on Variable Selection and LSTM |
title_fullStr | Research on Aviation Safety Prediction Based on Variable Selection and LSTM |
title_full_unstemmed | Research on Aviation Safety Prediction Based on Variable Selection and LSTM |
title_short | Research on Aviation Safety Prediction Based on Variable Selection and LSTM |
title_sort | research on aviation safety prediction based on variable selection and lstm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823347/ https://www.ncbi.nlm.nih.gov/pubmed/36616640 http://dx.doi.org/10.3390/s23010041 |
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