• DocumentCode
    2070973
  • Title

    Research on Drought Forecast Based on Rough Set Theory

  • Author

    Liu Zhao ; Qiao Chang-lu

  • Author_Institution
    Inst. of Water & Dev., Chang´an Univ., Xi´an, China
  • fYear
    2009
  • fDate
    26-28 Dec. 2009
  • Firstpage
    318
  • Lastpage
    322
  • Abstract
    The less predictable characteristics of droughts make drought both a hazard and a disaster: a hazard because it is a natural accident of unpredictable occurrence but of recognisable recurrence, and a disaster because it causes the disruption of the water supply to the natural and agricultural ecosystems as well as to other human activities. What make less predictable characteristics of droughts is its uncertainty, fuzzification and gray properties. Rough set theory was introduced to conduct drought forecast problem in the paper for its advantages in dealing with uncertain and incomplete problems. A case study was carried out, and the rules for drought forecast was acquired in the light of data selection, preprocessing, attributes reduction, rules generation and filtering, which could be used in drought forecast for the study area. In general, the results showed that it was quite successful with the application of rough set theory in the forecast of drought severity.
  • Keywords
    accidents; agricultural safety; disasters; rain; rough set theory; water supply; weather forecasting; agricultural ecosystems; disaster; drought forecast; fuzzification; gray properties; hazard; natural accident; recognisable recurrence; rough set theory; uncertainty; unpredictable occurrence; water supply disruption; Artificial neural networks; Hazards; Predictive models; Rivers; Set theory; Stochastic processes; Temperature; Uncertainty; Water; Wind forecasting; droughts forecast; knowledge discovery; rough set; rules generation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science and Engineering (ISISE), 2009 Second International Symposium on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-6325-1
  • Electronic_ISBN
    978-1-4244-6326-8
  • Type

    conf

  • DOI
    10.1109/ISISE.2009.61
  • Filename
    5447213