Research on DBN incorporating reinforcement learning for runway intrusion risk prediction
In order to solve the problems of difficulty in quantifying the risk of airport runway incursion events,poor timeliness and low accuracy,and to enhance the capability of predicting runway incursion risks,a DBN model incorporating reinforcement learning for risk prediction was constructed.Firstly,causal inference theory was combined with grey relational analysis to analyze historical runway incursion events and identify the underlying risk factors.Secondly,Bayesian network(BN)theory was applied to explore the correlations among these factors and quantify these correlations using the Pearson linear correlation coefficient.This process helped in constructing a causation correlations network that effectively represented the propagation of risks associated with runway incursions.Then,the triangular fuzzy method and Hidden Markov Models(HMMs)were utilized to further refine and optimize the DBN parameter learning mechanism.Finally,the model's accuracy was validated using historical data.The results demonstrate that the proposed model's predictions of runway incursion risks closely align with the statistical values of historical data,achieving an accuracy rate of 84％,which represents a significant 10％improvement over Bayesian network predictions.Additionally,the use of mutual information to identify key nodes is found to effectively improve accuracy and discrimination compared to the degree value evaluation method.
万方数据
维普
