Koskela, T., Lehtokangas, M., Saarinen, J., Kaski, K.: Time Series Prediction With Multilayer Perceptron, FIR and Elman Neural Networks. In: Proceedings of IEEE International Symposium on Intelligent Control 2001, Mexico City, Mexico (2001) ISBN 0-7803-6722-7 165–174 (1998) ISBN 1-85312-527-XĪcosta, G., Tosini, M.: A Firmware Digital Neural Network for Climate Prediction Applications. In: International Conference On Environmental Coastal Regions, Cancun, Mexico, vol. (2), pp. 1093–1098 (2010) ISBN 978-1-4244-5917-9Įbecken, F.F.: Fog Formation Prediction In Coastal Regions Using Data Mining Techniques.
METEOROLOGICAL PHENOMENA SOFTWARE
In: International Conference on Complex, Intelligent and Software Intensive Systems 2010, pp. Zazzaro, G., Pisano, F.M., Mercogliano, P.: Data Mining to Classify Fog Events by Applying Cost-Sensitive Classifier. The whole data mining process was managed by CRISP-DM methodology, one of the most accepted in this domain. The low cloud cover was forecasted at the national Slovak airport in Bratislava with decision trees. 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 selected algorithms were applied on obtained historical data from meteorological observations at several airports in United Arab Emirates and Slovakia. Our goal was to design, implement and evaluate a different approach based on suitable techniques and methods from data mining domain. In both these cases meteorologists already use some physical models based on differential equations as simulations. The management of air traffic at the airports was the main reason to design effective mechanisms for timely prediction of these phenomena. 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.
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