Author(s)

C. Choi, J. Ji, M. Yu, T. Lee, M. Kang & J. Yi

Abstract

In the conventional flood forecasting process, a rainfall–runoff model is used to predict runoff at a specific location. However, the process of determining the required parameters for the model is sometimes very complicated and requires extensive information and data. In addition, considerable amount of uncertainties may be included during the parameter estimation processes. Errors can occur during the pre-processing and main processing stages of the modeling, and errors from each step accumulate into the model result. In this study, a neuro-fuzzy technique is used to minimize the amount of uncertainties included in a conventional flood forecasting model for more accurate forecasting of floods. The adaptive neuro-fuzzy inference system (ANFIS), which is a data-driven model that combines a neural network and the fuzzy technique, can decrease the amount of physical data required for constructing a conventional model. By using only rainfall and water level data, ANFIS can easily construct and evaluate a flood forecasting model. Furthermore, the model construction process is relatively simple, and reliable results can be efficiently obtained in a reasonably short time once the model is developed. The developed model is applied to the Tancheon basin in Korea. The water level at the Daegok Bridge, which is located downstream of Tancheon, is forecast by the neuro-fuzzy method. The applicability and suitability of the model are studied by comparing the result with the observed stream level data from 2007 to 2011 in the Tancheon basin area. Tancheon is a tributary of the Han River and begins from the city of Yongin in Gyeonggi-do. It has a total length of 35.6 km and an area of 302 km2. The water level data from t + 1 to t + 18 is estimated by ANFIS using 10-min interval data. The results showed that the average height error was 24.48% and the average RMSE was 0.367 m. Keywords: ANFIS, neuro-fuzzy technique, flood forecasting.

Keywords

ANFIS, neuro-fuzzy technique, flood forecasting