摘要
机场管制是航空运输过程中至关重要的一环,其安全性对航空运输的可持续发展至关重要。针对机场管制系统运行过程中可能出现的风险进行预测,依据SHEL模型,构建管制系统风险指标体系,从体系中选取影响较大的冲偏出跑道、跑道侵入、跑道外接地三类事件,在传统的灰色预测GM(1,1)模型的基础上,加入BP神经网络组建GM-BP灰色神经网络预测模型进行风险预测。预测结果显示,所采用的GM-BP预测模型预测的精度较高,同时得益于其对数据的综合处理能力和对复杂系统的有效建模能力,在机场管制系统运行风险预警方面有着广泛的适用场景,为后续针对可能出现的风险实施风险管理提供了科学的参考。
Airport control is a crucial part of the air transportation process,and its safety is vital to the sustainable development of air transportation.This paper mainly focuses on predicting the risks that may occur during the operation of the airport control system,and the control system risk indicator system is constructed based on the SHEL model.Three types of events,namely,runway runout,runway incursion,and off-runway grounding,are selected from the system,and on the basis of the traditional gray prediction GM(1,1)model,BP neural network is added to form a GM-BP gray neural network prediction model for risk prediction.The prediction results show that the GM-BP prediction model used in this paper has high accuracy,and thanks to its comprehensive data processing capability and effective modeling capability for complex systems,it has a wide range of applicable scenarios in the operational risk warning of airport control system,which provides a scientific reference for the subsequent implementation of risk management for possible risks.
作者
佀庆民
赵永航
隋玉鲲
李俊艳
SI Qingmin;ZHAO Yonghang;SUI Yukun;LI Junyan(School of Civil Aviation,Zhengzhou University of Aeronautics,Zhengzhou 450046,China)
出处
《山东航空学院学报》
2024年第4期79-85,131,共8页
Journal of Binzhou University
基金
河南省高等学校青年骨干教师培养计划(2020GGJS174)
河南省高等学校重点科研项目(24A620005)。
关键词
灰色理论
神经网络
风险预测
航空安全
gray theory
neural network
risk prediction
aviation safety
