Weather visibility has an important impact on civil aviation to flight punctuality and safety. Sudden changes in weather visibility often cause flight go-around, holding, alternate, cancellation, and even unsafe incidents, so if visibility can be accurately predicted in advance, it will be significant to make accurate flight plans and pre-flight preparation. In autumn and winter, the visibility of plateau airports changes rapidly because of complex and changeable meteorological conditions, and traditional meteorological methods are also difficult to predict visibility changes accurately, so the flight risks and difficulty in flight planning increase. Considering that weather visibility is jointly determined by many meteorological factors, and there is a strong correlation between the time, based on long-term short-term memory network (LSTM), this paper uses the atmospheric state information of the airport ground station to construct a plateau airport visibility prediction model (PAVPM) to forecast the visibility of the plateau airport hourly in the next 1~6 hours. And finally we use hourly weather data from Urumqi Airport in winter of 2015-2019 to verify the validity of the model.


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    Title :

    Visibility Prediction of Plateau Airport Based on LSTM


    Contributors:
    Zixuan, Wu (author) / Qingchi, Ye (author) / Zhihong, Yi (author) / Yang, Wang (author) / Zhiming, Feng (author)


    Publication date :

    2021-03-12


    Size :

    2039038 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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