• DocumentCode
    3237912
  • Title

    Research of the Comprehensive Forecast of Hailstone

  • Author

    Wan Huisong ; Lu Zhiying ; Jiang Shuming ; Zhang Yuanyuan

  • Author_Institution
    Inf. Res. Inst. of Shandong Acad. of Sci., Jinan, China
  • fYear
    2012
  • fDate
    6-8 Nov. 2012
  • Firstpage
    423
  • Lastpage
    428
  • Abstract
    Rough Set Theory was used for data mining based on the characteristic database and can form a knowledge database for hailstone recognition to establish a single model for hailstone forecast. Thus the comprehensive hailstone forecasting model was formed. Firstly the rules discovered from Apriori algorithm were used to eliminate the interference, Secondly the integrated knowledge database was formed by the combination of the rules obtained from the Rough Set Theory and the FP-tree algorithm for the comprehensive forecast. Certainty factor model was adopted to solve the rule conflict which occurred as the number of the rule increased. The experimental results show that the comprehensive forecast model improved the accuracy of hailstone recognition and good results were achieved in practical operation.
  • Keywords
    atmospheric precipitation; data mining; geophysics computing; ice; knowledge based systems; rough set theory; trees (mathematics); weather forecasting; FP-tree algorithm; apriori algorithm; certainty factor model; characteristic database; data mining; hailstone forecasting model; hailstone recognition; knowledge database; rough set theory; rule conflict; Accuracy; Data mining; Databases; Forecasting; Interference; Predictive models; Set theory; Certainty factor model; Rough Sets Theory; the comprehensive forecast;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems (GCIS), 2012 Third Global Congress on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4673-3072-5
  • Type

    conf

  • DOI
    10.1109/GCIS.2012.67
  • Filename
    6449568