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http://dx.doi.org/10.3741/JKWRA.2011.44.7.523

Establishment and Application of Neuro-Fuzzy Real-Time Flood Forecasting Model by Linking Takagi-Sugeno Inference with Neural Network (I) : Selection of Optimal Input Data Combinations  

Choi, Seung-Yong (National Institute for Disaster Prevention)
Kim, Byung-Hyun (University of California Irvine)
Han, Kun-Yeun (School of Archi. & Civil Engineering, Kyungpook National Univ.)
Publication Information
Journal of Korea Water Resources Association / v.44, no.7, 2011 , pp. 523-536 More about this Journal
Abstract
The objective of this study is to develop the data driven model for the flood forecasting that are improved the problems of the existing hydrological model for flood forecasting in medium and small streams. Neuro-Fuzzy flood forecasting model which linked the Takagi-Sugeno fuzzy inference theory with neural network, that can forecast flood only by using the rainfall and flood level and discharge data without using lots of physical data that are necessary in existing hydrological rainfall-runoff model is established. The accuracy of flood forecasting using this model is determined by temporal distribution and number of used rainfall and water level as input data. So first of all, the various combinations of input data were constructed by using rainfall and water level to select optimal input data combination for applying Neuro-Fuzzy flood forecasting model. The forecasting results of each combination are compared and optimal input data combination for real-time flood forecasting is determined.
Keywords
Flood forecasting; Takagi-Sugeno fuzzy inference; Neural network; Optimal input data combination;
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