한국농공학회지 (Magazine of the Korean Society of Agricultural Engineers)

한국농공학회 (The Korean Society of Agricultural Engineers)
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Short-term Flood Forecasting Using Artificial Neural Networks

  • 강문성 (서울대학교 농업생명과학연구원) ;
  • 박승우 (서울대학교 농공대학)
  • 발행 : 2003.03.01
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초록

An artificial neural network model was developed to analyze and forecast Short-term river runoff from the Naju watershed, in Korea. Error back propagation neural networks (EBPN) of hourly rainfall and runoff data were found to have a high performance In forecasting runoff. The number of hidden nodes were optimized using total error and Bayesian information criterion. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$is greater than 0.99) for calibration and verification data sets. Increasing the time horizon for application data sets, thus mating the model suitable for flood forecasting. decreases the accuracy of the model. The resulting optimal EBPN models for forecasting hourly runoff consists of ten rainfall and four runoff data(ANN0410 model) and ten rainfall and ten runoff data(ANN1010 model). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$is greater than 0.92).

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참고문헌

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