摘要
Abstract
In response to the problems of time-consuming,labor-intensive,and low accuracy in predicting low visibility at existing airports,this paper proposes a neural network prediction model that integrates self-attention mechanism.The hourly mes-sage data partitions of Chongqing Jiangbei International Airport from April 2003 to April 2023 for the past 20 years are used as in-puts to achieve low visibility prediction for different step sizes in the future.Under the framework of the traditional neural network RNN,LSTM and GRU,the constructed model integrates the self-attention mechanism(self-attention)and ProbSparse(Probabili-ty Sparse)self-attention mechanism respectively,extracts the information of the input data sequence and models the complex non-linear relationship between factors affecting low visibility.Finally,the average absolute error(MAE),the root mean square error(RMSE)and symmetric mean absolute percentage error(SMAPE)are used as evaluation indicators for the nine models mentioned above.The results show that in the two low visibility intervals,the prediction models MAE,RMSE,and SMAPE fused with self-at-tention mechanism have decreased by more than 7.3%,1.86%,and 4.6%,respectively.When low visibility weather occurs again at the airport,corresponding network models can be selected according to the different output step sizes to achieve more accurate pre-dictions.关键词
低能见度预测/自注意力机制/概率稀疏自注意力机制/循环神经网络/机场安全运行Key words
low visibility prediction/self-attention mechanism/probability sparse self-attention mechanism/recurrent neu-ral network/airport safety operation分类
计算机与自动化
