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|Prediction of Hail Suppression Program Seeding Parameters using Data Mining Techniques
|FRASCATI::Natural sciences::Computer and information sciences
|Proceedings of the 1st International Scientific Conference, eRA: The Contribution of Information Technology to Science, Economy, Society and Education, Tripolis, Greece
|In this study we examine the existence of interesting patterns among the Greek National Hail Suppression Program (GNHSP) data using Data Mining techniques. Two groups of GNHSP data are used. The hailstorms data, containing the values of some hailstorm attributes, such as type, life time, intensity, size, motion and the seeding data, containing the values of some seeding parameters, such as seeding time duration, seeding material mass consumption and mean seeding rate. The results we obtain in the form of association rules can contribute to the prediction of seeding parameters from storm data and the determination of hailstorm characteristics from seeding data.
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|Department of Applied Informatics
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