Runway visual range prediction using Convolutional Neural Network with Weather information

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.

Figure 1. CNN model design for runway visual range forecast

Figure 2. Training and testing errors by epoch

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

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