Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1256
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dc.contributor.authorTsagalidis, Evangelos-
dc.contributor.authorEvangelidis, Georgios-
dc.date.accessioned2022-08-30T13:22:49Z-
dc.date.available2022-08-30T13:22:49Z-
dc.date.issued2006-
dc.identifier.urihttp://ikaros.teipir.gr/era/06072006/sessions/b.3.information_management_session/full_papers/b.3.6.docen_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/1256-
dc.description.abstractIn 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.en_US
dc.language.isoenen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.titlePrediction of Hail Suppression Program Seeding Parameters using Data Mining Techniquesen_US
dc.typeConference Paperen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.firstpage322en_US
local.identifier.lastpage329en_US
local.identifier.volumetitleProceedings of the 1st International Scientific Conference, eRA: The Contribution of Information Technology to Science, Economy, Society and Education, Tripolis, Greeceen_US
Appears in Collections:Department of Applied Informatics

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