Research on the Evaluation of the Effectiveness of Operational Risk Management for Medium or Small Airports
As key transportation hubs and economic engines,airports must implement security risk man-agement to ensure safe operations.Therefore,this study proposes an evaluation model for the effectiveness of hazard control measures,which firstly collects hazard source data from multiple airports,quantitatively analyzes them,and then uses GloVe to process the text of control measures into word vectors,and uses the text convolutional neural network(TextCNN)model to realize text classification and evaluate the effect of risk control measures.Finally,the advantages and disadvantages of the efficacy evaluation model of hazard control measures were determined.The results show that the accuracy of the performance evaluation model based on TextCNN is 74.36%,while that of the model based on TextRNN is 69.24%.Therefore,TextC-NN is more suitable for such evaluation tasks.This study provides a new perspective and approach for air-port authorities to more effectively identify,and manage various sources of hazard,thereby improving the safety performance of the entire airport.
airport risk controlconvolutional neural networkhazard sourceefficacy evaluationnatural language processingeffectiveness evaluation
万方数据
维普
