Meteorological Phenomena Forecast Using Data Mining Prediction Methods

The occurrence of various meteorological phenomena, such as fog or low cloud cover, has significant impact on many human activities as air or ship transport operations. The management of air traffic at the airports was the main reason to design effective mechanisms for timely prediction of these phenomena. In both these cases meteorologists already use some physical models based on differential equations as simulations. Our goal was to design, implement and evaluate a different approach based on suitable techniques and methods from data mining domain. The selected algorithms were applied on obtained historical data from meteorological observations at several airports in United Arab Emirates and Slovakia. In the first case, the fog occurrence was predicted based on data from METAR messages with algorithms based on neural networks and decision trees. The low cloud cover was forecasted at the national Slovak airport in Bratislava with decision trees. The whole data mining process was managed by CRISP-DM methodology, one of the most accepted in this domain.