Long-term prediction of visibility using TimeGAN-Informer
The accurate prediction of visibility is pivotal for airport operations,guiding crucial business decisions,and ensuring the safe takeoff and landing of aircraft.However,existing visibility prediction models often face limitations in providing sufficiently long-term forecasts.To address this challenge,this paper introduces a novel method for long-term visibility prediction at airports,leveraging the innovative combination of TimeGAN and Informer,termed TimeGAN-Informer.When dealing with a dataset containing a limited number of samples,the risk of overfitting becomes a concern.Utilizing the Time series Generative Adversarial Network(TimeGAN)can effectively mitigate this issue by expanding the dataset,thereby enhancing the accuracy of the deep learning model.Consequently,for this study,meteorological and pollutant data from Tianjin Binhai Airport and Tianjin spanning from 2018 to 2022 were selected as research data.Through methods such as correlation coefficient analysis and recursive feature elimination,the main influencing factors on visibility were extracted.Subsequently,the visibility time series data were augmented using TimeGAN,followed by employing the long time series prediction model,Informer,for visibility prediction.The findings indicate that for prediction steps of 1 day,2 days,and 3 days,the Mean Absolute Error(MAE)of the TimeGAN-Informer model were 2.42,3.13,and 3.57,respectively.Compared to the Informer model,this represented a reduction of 0.29,0.27,and 0.28,respectively.Similarly,compared to the LSTM model,reductions of 0.28,0.49,and 0.63 were observed.Moreover,the Root Mean Square Error(RMSE)values were 3.03,3.7,and 4.09,respectively.In comparison to the Informer model,this indicated reductions of 0.38,0.22,and 0.24,respectively.Likewise,compared to the LSTM model,reductions of 0.3,0.5,and 1.04 were observed.Additionally,the percentage of errors less than 30％accounted for 78.07％,70.68％,and 63.84％of the test sample set,respectively.Despite the decline in prediction performance as the prediction step size increases,the majority of samples still exhibit prediction errors of less than 30％when the prediction step size is extended to 3 days,indicating relatively accurate long-term predictions.In conclusion,the TimeGAN-Informer model proposed in this study demonstrates good performance in long-term visibility prediction and holds potential for application in airport visibility forecasting.
safety engineeringvisibility forecastdata expansionInformertime series
NETL
NSTL
万方数据
