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http://dx.doi.org/10.3741/JKWRA.2014.47.2.207

Flood Forecasting Study using Neural Network Theory and Hydraulic Routing  

Jee, Hong Kee (Department of Civil Engineering, Yeungnam University)
Choo, Yeon Moon (Department of Civil Engineering, Yeungnam University)
Publication Information
Journal of Korea Water Resources Association / v.47, no.2, 2014 , pp. 207-221 More about this Journal
Abstract
Recently, due to global warming, climate change has affected short time concentrated local rain and unexpected heavy rain which is increasingly causing life and property damage. Therefore, this paper studies the characteristic of localized heavy rain and flash flood in Nakdong basin study area by applying Data Mining method to predict flood and constructing water level predicting model. For the verification neural network from Data Mining method and hydraulic flood routing was used for flood from July 1989 to September 1999 in Nakdong point and Iseon point was used to compare flood level change between observed water level and SAM (Slope Area Method). In this research, the study area was divided into three cases in which each point's flood discharge, water level was considered to construct the model for hydraulic flood routing and neural network based on artificial intelligence which can be made from simple input data used for comparison analysis and comparison evaluation according to actual water level and from the model.
Keywords
datamining method; neural network theory; flood predict; flood routing;
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Times Cited By KSCI : 6  (Citation Analysis)
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