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KMID : 1024520060150020141
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Journal of the Environmental Sciences
2006 Volume.15 No. 2 p.141 ~ p.155
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Development of a Runoff Forecasting Model Using Artificial Intelligence
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Lim Kee-Seok
Heo Chang-Hwan
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Abstract
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This study is aimed at the development of a runoff forecasting model to solve the uncertainties occurring in the process of rainfall-runoff modeling and improve the modeling accuracy of the stream runoff forecasting, The study area is the downstream of Naeseung-chun. Therefore, time-dependent data was obtained from the Wolpo water level gauging station. 11 and 2 out of total 13 flood events were selected for the training and testing set of model. The model performance was improved as the measuring time interval was smaller than the sampling time interval. The Neuro-Fuzzy(NF) and TANK models can give more accurate runoff forecasts up to 4 hours ahead than the Feed Forward Multilayer Neural Network(FFNN) model in standard above the Determination coefficient 0.7.
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KEYWORD
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Neuro-Fuzzy, Feed Forward Multilayer Neural Network, TANK model
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