Minglan Xiong, Huawei Wang, Changchang Che, Ruiguan Lin
{"title":"迈向更安全的航空:GA-XGBoost-SHAP在事件认知和模型可解释性中的应用","authors":"Minglan Xiong, Huawei Wang, Changchang Che, Ruiguan Lin","doi":"10.1177/1748006x231205498","DOIUrl":null,"url":null,"abstract":"Flight incidents are characterized by complex mechanisms, leading to poor prediction model robustness and explainability. Based on the full-dimensional description of flight incidents, the explainable module is added to the prediction model to achieve its accuracy, stability, and explainability. Firstly, imbalance processing is performed employing the sampling method, and a genetic algorithm (GA) is applied for feature selection; these results are then considered as prediction model input. Secondly, an extreme gradient boosting algorithm (XGBoost)-based incident severity prediction model is established with five categories of none, minor, serious, fatal, and total as prediction labels; real data is used for validation, and the model shows good robustness and superiority. Finally, the SHapley Additive exPlanation (SHAP) is introduced to explain the correlation between incidents severity and input features and to measure feature importance. The results show that the proposed method has higher prediction accuracy and robustness. Which can provide some decision-making reference for aviation operation management departments to emergencies, learn the deep-seated law of incidents, and promote the paradigm of active safety management.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Toward safer aviation: Application of GA-XGBoost-SHAP for incident cognition and model explainability\",\"authors\":\"Minglan Xiong, Huawei Wang, Changchang Che, Ruiguan Lin\",\"doi\":\"10.1177/1748006x231205498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Flight incidents are characterized by complex mechanisms, leading to poor prediction model robustness and explainability. Based on the full-dimensional description of flight incidents, the explainable module is added to the prediction model to achieve its accuracy, stability, and explainability. Firstly, imbalance processing is performed employing the sampling method, and a genetic algorithm (GA) is applied for feature selection; these results are then considered as prediction model input. Secondly, an extreme gradient boosting algorithm (XGBoost)-based incident severity prediction model is established with five categories of none, minor, serious, fatal, and total as prediction labels; real data is used for validation, and the model shows good robustness and superiority. Finally, the SHapley Additive exPlanation (SHAP) is introduced to explain the correlation between incidents severity and input features and to measure feature importance. The results show that the proposed method has higher prediction accuracy and robustness. Which can provide some decision-making reference for aviation operation management departments to emergencies, learn the deep-seated law of incidents, and promote the paradigm of active safety management.\",\"PeriodicalId\":51266,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/1748006x231205498\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/1748006x231205498","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Toward safer aviation: Application of GA-XGBoost-SHAP for incident cognition and model explainability
Flight incidents are characterized by complex mechanisms, leading to poor prediction model robustness and explainability. Based on the full-dimensional description of flight incidents, the explainable module is added to the prediction model to achieve its accuracy, stability, and explainability. Firstly, imbalance processing is performed employing the sampling method, and a genetic algorithm (GA) is applied for feature selection; these results are then considered as prediction model input. Secondly, an extreme gradient boosting algorithm (XGBoost)-based incident severity prediction model is established with five categories of none, minor, serious, fatal, and total as prediction labels; real data is used for validation, and the model shows good robustness and superiority. Finally, the SHapley Additive exPlanation (SHAP) is introduced to explain the correlation between incidents severity and input features and to measure feature importance. The results show that the proposed method has higher prediction accuracy and robustness. Which can provide some decision-making reference for aviation operation management departments to emergencies, learn the deep-seated law of incidents, and promote the paradigm of active safety management.
期刊介绍:
The Journal of Risk and Reliability is for researchers and practitioners who are involved in the field of risk analysis and reliability engineering. The remit of the Journal covers concepts, theories, principles, approaches, methods and models for the proper understanding, assessment, characterisation and management of the risk and reliability of engineering systems. The journal welcomes papers which are based on mathematical and probabilistic analysis, simulation and/or optimisation, as well as works highlighting conceptual and managerial issues. Papers that provide perspectives on current practices and methods, and how to improve these, are also welcome