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http://dx.doi.org/10.3741/JKWRA.2013.46.8.857

Inflow Estimation into Chungju Reservoir Using RADAR Forecasted Precipitation Data and ANFIS  

Choi, Changwon (Ajou Univ. Division of Civil and Trans. Engineering)
Yi, Jaeeung (Ajou University, Division of Construction Engineering)
Publication Information
Journal of Korea Water Resources Association / v.46, no.8, 2013 , pp. 857-871 More about this Journal
Abstract
The interest in rainfall observation and forecasting using remote sensing method like RADAR (Radio Detection and Ranging) and satellite image is increased according to increased damage by rapid weather change like regional torrential rain and flash flood. In this study, the basin runoff was calculated using adaptive neuro-fuzzy technique, one of the data driven model and MAPLE (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as one of the input variables. The flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated. Six rainfall events occurred at flood season in 2010 and 2011 in Chungju Reservoir basin were used for the input data. The flood estimation results according to the rainfall data used as training, checking and testing data in the model setup process were compared. The 15 models were composed of combination of the input variables and the results according to change of clustering methods were compared and analysed. From this study was that using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation. The model using MAPLE forecasted precipitation data showed relatively better result at inflow estimation Chungju Reservoir.
Keywords
RADAR; MAPLE; ANFIS; flood estimation;
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