• Title/Summary/Keyword: modclark

Search Result 1, Processing Time 0.016 seconds

Radar Rainfall Adjustment by Artificial Neural Network and Runoff Analysis (신경망에 의한 레이더강우 보정 및 유출해석)

  • Kim, Soo Jun;Kwon, Young Soo;Lee, Keon Haeng;Kim, Hung Soo
    • KSCE Journal of Civil and Environmental Engineering Research
    • /
    • v.30 no.2B
    • /
    • pp.159-167
    • /
    • 2010
  • The purpose of this study is to get the adjusted radar rainfalls by ANN(Artificial Neural Network) method. In the case of radar rainfall, it has an advantage of spatial distribution characteristics of rainfall while point rainfall has an advantage at the point. Therefore we adjusted the radar rainfall by ANN method considering the advantages of two rainfalls of radar and point. This study constructed two ANN models of Model I and Model II for radar rainfall adjustment. We collected the three rainfall events and adjusted the radar rainfall for Anseong-cheon basin. The two events were inputted into the Modeland Model to derive the optimum parameters and the rest event was used for validation. The adjusted radar rainfalls by ANN method and the raw radar rainfall were used as the input data of ModClark model which is a semi-distributed model to simulate the runoff. As the results of the simulation, the runoff by raw radar rainfall were overestimated but the peak time and peak runoff from the adjusted rainfall by ANN were well fitted to the observed hydrograph.