A Method for Predicting Fog and Identifying Its Type Based on Neural Networks for the Saint Petersburg (Pulkovo) Airfield

Abstract

Fogs have a serious impact on human activity, in particular, on aviation, since they significantly impair visibility and therefore make aircraft landing difficult. In most cases, fogs cause irregularity of flights and sometimes lead to disasters, so timely and accurate forecasting of the onset of fog and its type is very important. At present, numerical methods greatly facilitate the forecasters’ work, but the problem of predicting visibility and fog remains relevant. Artificial intelligence technologies, in particular, deep learning algorithms using various kinds of neural networks are currently becoming more widespread in hydrometeorological activities. In the present study, the main objective is to develop a method for predicting the appearance of fog and to identify its type based on neural networks. The results of testing the method have showed its practical usefulness.