Meteorological Analysis and Prediction of Gusts at Istanbul Airport Using Machine Learning Algorithms

Wind gust is a sudden meteorological weather phenomenon. It can cause many material and moral accidents, especially if it occurs during aircraft take-off and landing at airports. In this study, gust analysis and gust prediction for Istanbul Airport were performed using machine learning algorithms. Metar data of Istanbul Airport between 01.11.2018 and 31.12.2024 were used in the study. When this Metar data was analysed, it was found that on average between 250 and 300 Gust events were reported annually.  Gust values were found to vary between 11 and 65 knots. It was reported that the highest number of gust events was reported in November with 179 times and the lowest number was reported in August with 38 times. When the gust intensities are analyzed, it is seen that the strongest gusts occurred in February. When the gusts were analyzed hourly, it was found that most gusts occurred between 01.00 and 03.00 hours. The most severe gusts occurred between 15.00 and 20.00. In the study, the relationship between gusts and other meteorological variables such as temperature, pressure, dew point temperature was analyzed. In the other part of the study, three different machine learning methods Random Forest (RF), long-short term memory (LSTM) and extreme gradient boosting (XGB) were used to predict gusts. In these methods, models were derived and evaluated on 1000 different randomly selected subsets, 70% for training and 30% for testing. It was observed that the prediction success of the three different models used in the study increased at times of high wind gust values (≥ 30 knots), while the prediction success was lower at times of low wind gust values.