Air Turbulence Forecasting of Airbus type A320 in Southeast Asia using Deep Learning Method

This paper is present the Air Turbulence Forecasting of Airbus type A320 in Southeast Asia using the Deep Learning Method. It will collect flight data of aircraft in the region of Southeast Asia (Thailand and Vietnam). In which data was collected for 40 flights to forecast the occurrence of air turbulence in 3 status consist of non-turbulence, the rapid decrease and increase the altitude is out of pilot control. In this research using deep learning with supervise learning and create the mathematical model for air turbulence forecasting which could help to reduce the wastage that may affect to the passenger. The results, using 5 layers of deep learning (1 input layer, 3 hidden layers, and 1 output layer). The most suitable model consists of 9 features such as vertical speed, calibrated altitude, wind speed, wind direction (wind angle), temperature, latitude, longitude, true airspeed, and indicated airspeed. The output layer consists of 3 classes (class1=non-turbulence, class2=increase altitude, and class3=decrease altitude) and optimization the weight with gradient descent. The epoch number is 1500 and the learning rate is 0.1, which will get accuracy 88% for the train set and 86 % for the test set.