Flood forecasting technology with radar-derived rainfall data using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Watanabe:2009:IJCNN,
  author =       "Naoki Watanabe and Kazuhiko Fukami and 
                 Hitoki Imamura and Katsuki Sonoda and Soichiro Yamane",
  title =        "Flood forecasting technology with radar-derived
                 rainfall data using Genetic Programming",
  booktitle =    "International Joint Conference on Neural Networks,
                 IJCNN 2009",
  year =         "2009",
  pages =        "3311--3318",
  address =      "Atlanta, Georgia, USA",
  month =        jun # " 14-19",
  keywords =     "genetic algorithms, genetic programming, floods,
                 hydrological techniques, radarGMDH, flood disasters,
                 flood forecasting technology, radar-derived rainfall,
                 radar-derived rainfall data, water level forecasting
                 model, water-level prediction",
  DOI =          "doi:10.1109/IJCNN.2009.5178691",
  abstract =     "Implementation of flood forecasting system is crucial
                 for reducing flood disasters urgently and effectively.
                 The authors propose a new method of flood forecasting
                 using genetic programming (GP) and GMDH. Traditional
                 method based on physical model takes time to analyze
                 the hydrologic and hydraulic characteristics of a
                 river, but the new method has potential to make a water
                 level forecasting model from ground-based or
                 radar-derived rainfall automatically by learning the
                 past data of river water level or dam inflow and
                 rainfall, which will be useful in particular for
                 medium-to-small scale rivers. Case studies were
                 conducted for the water-level prediction at the Saba
                 and the Onga Rivers in Japan. The results from both the
                 case studies were encouraging to promote the new
                 method, because the water-level predictions with 6-hour
                 lead time were relatively good. Furthermore,
                 comparative analysis about the incorporation of spatial
                 distribution of rainfall in the upstream brought out
                 the necessity of the combined incorporation of both
                 direct and averaging area for better accuracy.",
  notes =        "also known as \cite{5178691}",
}

Genetic Programming entries for Naoki Watanabe Kazuhiko Fukami Hitoki Imamura Katsuki Sonoda Soichiro Yamane

Citations