For the prediction of multi-site rainfall with radar data and ground meteorological data, a rainfall prediction model was proposed, which uses the neural network theory, a kind of artifical intelligence technique. The input layer of the prediction model was constructed with current ground meteorological data, their variation, moving vectors of rainfall field and digital terrain of the measuring site, and the output layer was constructed with the predicted rainfall up to 3 hours. In the application of the prediction model to the Pyungchang river basin, the learning results of neural network prediction model showed more improved results than the parameter estimation results of an existing physically based model. And the proposed model comparisonally well predicted the time distribution of rainfall.

• 정재성(한국수자원공사 댐관리처) | Jae-Sung Jung (Dam Management Division, Korea Water Resources Corporation)
• 이장춘(이리농공전문대학 토목공학과) | Jang-Choon Lee (Dept. of Civil Engineering, Iri National College of Agriculture and Technology)
• 박영기(이리농공전문대학 토목공학과) | Young-Ki Park (Dept. of Civil Engineering, Iri National College of Agriculture and Technology)