Browse > Article
http://dx.doi.org/10.3741/JKWRA.2018.51.2.165

TFN model application for hourly flood prediction of small river  

Sung, Ji Youn (Han River Flood Control Office, Ministry of Land, Infrastructure and Transport (MOLIT))
Heo, Jun-Haeng (School of Civil and Environmental Engineering, Yonsei University)
Publication Information
Journal of Korea Water Resources Association / v.51, no.2, 2018 , pp. 165-174 More about this Journal
Abstract
The model using time series data can be considered as a flood forecasting model of a small river due to its efficiency for model development and the advantage of rapid simulation for securing predicted time when reliable data are obtained. Transfer Function Noise (TFN) model has been applied hourly flood forecast in Italy, and UK since 1970s, while it has mainly been used for long-term simulations in daily or monthly basis in Korea. Recently, accumulating hydrological data with good quality have made it possible to simulate hourly flood prediction. The purpose of this study is to assess the TFN model applicability that can reflect exogenous variables by combining dynamic system and error term to reduce prediction error for tributary rivers. TFN model with hourly data had better results than result from Storage Function Model (SFM), according to the flood events. And it is expected to expand to similar sized streams in the future.
Keywords
Hourly flood prediction; TFN model; Time series model; Small scale river;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Anselmo, V., and Ubertini, L. (1979). "Transfer function-noise model applied to flow forecasting." Hydrological Sciences-Bulletin, Vol. 24, No. 3, pp. 353-359.   DOI
2 Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1976). Time series analysis: forecasting and control, Prentice-Hall International, Inc.
3 Choi, S. Y., and Han, K. Y. (2011). "Comparison and analysis for performance of flood stage prediction regression model according to type of input rainfall." Korean Society of Hazard Mitigation, Vol. 11, No. 5, pp. 313-325.   DOI
4 Chung, G., Park, H. S., Sung, J. Y., and Kim, H. J. (2012). "Determination and evaluation of optimal parameters in storage function method using SCE-UA." Journal of Korea Water Resources Association, Vol. 45, No. 11, pp. 1169-1186.   DOI
5 Jeong, D. K., and Lee, B. H. (2010). "Development of urban flood water level forecasting model using regression method." Journal of Korea Water Resources Association, Vol. 30, No. 4-B, pp. 347-359.
6 Kang, K. S., and Heo, J. H. (2006). "Comparative study on method of stochastic modeling in Han river basin." Proceedings 2006 Korea Water Rersources Conference, Jeju, Korea, pp. 669-673.
7 Kimura, T. (1961). The flood runoff analysis method by the storage function model. The Public Works. Research Institute, Ministry of Construction.
8 Park, J., Kwon, J. H, Kim, T., and Heo, J. H. (2014). "Future inflow simulation considering the uncertainties of TFN model and GCMs on Chungju dam basin." Journal of Water Resources Association, Vol. 47, No. 2, pp. 135-143.   DOI
9 Kumar, A. P. S., Sudheer, K. P., Jain, S. K., and Agarwal P. K. (2005). "Rainfall-runoff modeling using artificial neural networks: comparison of network types." Hydrological Processes, Vol. 19, No. 6, pp. 1277-1291.   DOI
10 Lohani, A. K., Goel, N. K., and Bhatia, K. K. S. (2011). "Comparative study of neural network, fuzzy logic and linear transfer function techniques in daily rainfall-runoff modeling under different input domains." Hydrological Processes, Vol. 25, No. 2, pp. 175-193.   DOI
11 R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
12 Salas, J. D., Delleur, J. W., YevJevich, V., and Lane, W. L.(1980). Applied modeling of hydrologic time series, Water Resources Publications, Littleton, Colorado, pp. 461-473.
13 Shamseldin, A. Y. (2005). River basin modelling for flood risk mitigation.
14 Hipel, K. W. (1994). Developments in water science: time series modelling of water resources and environmental systems, Elsevier.
15 Yoon, K., and Kim, T. (2003). "Development of the multiple regression runoff model using rainfall forecast data by radar." Proceedings 2003 The Korean Society of Civil Engineers Conference, pp. 2187-2198.
16 Song, J. H., Chung, G., and Kang, M. S. (2014). "An introduction of a parameter optimization method for watershed models using MATLAB." Rural Resources, Vol. 56, No. 2, pp. 16-25 (In Korean).
17 Song, J. H., Song, I., Kim, J. T., and Kang, M. S. (2015). "Simulation of agricultural water supply considering yearly variation of irrigation efficiency." Journal of Korea Water Resources Association, Vol. 48, No. 6 pp. 425-438 (in Korean).   DOI
18 Tokar, A. S., and Johnson, A. (1999). "Rainfall-runoff modeling using artificial neural networks. Journal of Hydrology, Vol. 4, No. 3, pp. 232-239.
19 Tokar, A. S., and Markus, M. (2000). "Precipitation-runoff modeling using artificial neural networks and conceptual models." Journal of Hydrology, Vol. 5, No. 2, pp. 156-161.
20 Wood, E. F., and O'connell, P. E. (1985). Hydrological forecasting. A Wiley-Interscience Publication, pp. 505-558.
21 Young, P. (1984). Recursive estimation and time series analysis. Springer, Berlin, pp. 198-228.