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http://dx.doi.org/10.5322/JES.2006.15.2.141

Development of a Runoff Forecasting Model Using Artificial Intelligence  

Lim Kee-Seok (Environment, Forestry & Fishery Department, Gyeongsangbukdo Province)
Heo Chang-Hwan (Researcher, EIA Division, Korea Environment Institute)
Publication Information
Journal of Environmental Science International / v.15, no.2, 2006 , pp. 141-155 More about this Journal
Abstract
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$(T_m)$ was smaller than the sampling time interval$(T_s)$. 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$(R^2)$ 0.7.
Keywords
Neuro-Fuzzy; Feed Forward Multilayer Neural Network; TANK model;
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