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http://dx.doi.org/10.3741/JKWRA.2008.41.3.341

Flood Forecasting and Warning Using Neuro-Fuzzy Inference Technique  

Yi, Jae-Eung (Dept. of Civil & Transportation Engrg., Ajou Univ.)
Choi, Chang-Won (Dept. of Civil & Transportation Engrg., Ajou Univ.)
Publication Information
Journal of Korea Water Resources Association / v.41, no.3, 2008 , pp. 341-351 More about this Journal
Abstract
Since the damage from the torrential rain increases recently due to climate change and global warming, the significance of flood forecasting and warning becomes important in medium and small streams as well as large river. Through the preprocess and main processes for estimating runoff, diverse errors occur and are accumulated, so that the outcome contains the errors in the existing flood forecasting and warning method. And estimating the parameters needed for runoff models requires a lot of data and the processes contain various uncertainty. In order to overcome the difficulties of the existing flood forecasting and warning system and the uncertainty problem, ANFIS(Adaptive Neuro-Fuzzy Inference System) technique has been presented in this study. ANFIS, a data driven model using the fuzzy inference theory with neural network, can forecast stream level only by using the precipitation and stream level data in catchment without using a lot of physical data that are necessary in existing physical model. Time series data for precipitation and stream level are used as input, and stream levels for t+1, t+2, and t+3 are forecasted with this model. The applicability and the appropriateness of the model is examined by actual rainfall and stream level data from 2003 to 2005 in the Tancheon catchment area. The results of applying ANFIS to the Tancheon catchment area for the actual data show that the stream level can be simulated without large error.
Keywords
ANFIS; neural network; data driven model; flood forecasting;
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