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http://dx.doi.org/10.3741/JKWRA.2004.37.9.771

A Development of System for Flood Runoff Forecasting using Neural Network Model  

Ahn, Sang-Jin (충북대학교 토목공학과)
Jun, Kye-Won (삼척대학교 방재기술전문대학원)
Publication Information
Journal of Korea Water Resources Association / v.37, no.9, 2004 , pp. 771-780 More about this Journal
Abstract
The purpose of this study is to test a development of system for flood runoff forecasting using neural network model. As the forecasting models for flood runoff the neural network model was tested with the observed flood data at Gongju and Buyeo stations. The neural network model consists of input layer, hidden layer, and output layer. For the flood events tested rainfall and runoff data were the input to the input layer and the flood runoff data were used in the output layer. To make a choice the forecasting model which would make up of runoff forecasting system properly, real-time runoff of river when flood periods were forecasted by using neural network model and state-space model. A comparison of the results obtained by the two forecasting models indicated the superiority and reliability of the neural network model over the state-space model. The neural network model was modified to work in the Web and developed to be the basic model of the forecasting system for the flood runoff. The neural network model developed to be used in the Web was loaded into the server and was applied to the main stream of Geum river. For the main stage gauging stations mentioned above the applicability of the selected forecasting model, the Neural Network Model, was verified in the Web.
Keywords
Neural Network; Flood Runoff; Web; System;
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