Water Engineering Research

Korea Water Resources Association (한국수자원학회)
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FLASH FLOOD FORECASTING USING ReMOTELY SENSED INFORMATION AND NEURAL NETWORKS PART I : MODEL DEVELOPMENT

  • Kim, Gwang-seob (Department of Civil Engineering, Kyungpook National University) ;
  • Lee, Jong-Seok (Utah Water Research Laboratory, College of Engineering, Utah State University, Logan, U.S.A.)
  • Published : 2002.04.01
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Abstract

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.

Keywords

References

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