International Journal of Advanced Culture Technology

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
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Runway visual range prediction using Convolutional Neural Network with Weather information

  • Ku, SungKwan (Department of Aviation industrial and System Engineering, Hanseo University) ;
  • Kim, Seungsu (Institute for Intelligent Systems and Robotics (ISIR), Sorbonne University) ;
  • Hong, Seokmin (Department of Unmanned Aircraft Systems, Hanseo University)
  • Received : 2018.11.28
  • Accepted : 2018.12.10
  • Published : 2018.12.31
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Abstract

The runway visual range is one of the important factors that decide the possibility of taking offs and landings of the airplane at local airports. The runway visual range is affected by weather conditions like fog, wind, etc. The pilots and aviation related workers check a local weather forecast such as runway visual range for safe flight. However there are several local airfields at which no other forecasting functions are provided due to realistic problems like the deterioration, breakdown, expensive purchasing cost of the measurement equipment. To this end, this study proposes a prediction model of runway visual range for a local airport by applying convolutional neural network that has been most commonly used for image/video recognition, image classification, natural language processing and so on to the prediction of runway visual range. For constituting the prediction model, we use the previous time series data of wind speed, humidity, temperature and runway visibility. This paper shows the usefulness of the proposed prediction model of runway visual range by comparing with the measured data.

Keywords

E1GMBY_2018_v6n4_190_f0001.png 이미지

Figure 1. CNN model design for runway visual range forecast

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Figure 2. Training and testing errors by epoch

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Figure 3 Runway visual range forecast (1 hour ahead) for 1 month

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Figure 4 runway visual range magnified from 7000 tick to 12000tick of Figure 3

References

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