Analysis Method of Civil Aviation Unplanned Events Based on CNN-LSTM Hybrid Model
Safety is the core theme of the civil aviation industry,and unplanned events are an important source of information for identifying safety hazards and improving aviation safety.The unstructured and large number of unplanned events make manual analysis difficult and inefficient.In order to improve the analysis efficiency and accuracy of unplanned events,this paper proposes a hybrid deep neural network model based on CNN-LSTM,which is used for the automated analysis of civil aviation unplanned events.It is compared with SVM,CNN and LSTM models,trained on the airline event log sample data set,and judged the event classification results.The experimental results show that the proposed CNN-LSTM hybrid model has the highest classification accu-racy,and has the most stable classification performance for unbalanced data samples.
deep learningcivil aviation safetytext analysisconvolutional neural networklong short-term memory
国家科技期刊平台
NSTL
万方数据
