Reliability evaluations of time of concentration using artificial neural network model -focusing on Oncheoncheon basin-

Euihyeok Yoon; Jongbin Park; Jaehyuk Lee; Hyunsuk Shin

doi:10.3741/JKWRA.2018.51.1.71

All Issue

2018 Vol.51, Issue 1 Preview Page Next Page

Reliability evaluations of time of concentration using artificial neural network model -focusing on Oncheoncheon basin- 인공신경망 모형을 이용한 도달시간의 신뢰성 평가 -온천천 유역을 대상으로-

31 January 2018. pp. 71-80

PDF XML

Abstract

For the stream management, time of concentration is one of the important factors. In particular, as the requirement about various application of the stream increased, accuracy assessment of concentration time in the stream as waterfront area is extremely important for securing evacuation at the flood. the past studies for the assessment of concentration time, however, were only performed on the single hydrological event in the complex basin of natural streams. The development of a assessment methods for the concentration time on the complex hydrological event in a single watershed of urban streams is insufficient. Therefore, we estimated the concentration time using the rainfall- runoff data for the past 10 years (2006~2015) for the Oncheon stream, the representative stream of the Busan, where frequent flood were taken place by heavy rains, in addition, reviewed the reliability using artificial neural network method based on Matlab. We classified a total of 254 rainfalls events based on over unrained 12 hours. Based on the classification, we estimated 6 parameters (total precipitation, total runoff, peak precipitation/ total precipitation, lag time, time of concentration) to utilize for the training and validation of artificial neural network model. Consequently, correlation of the parameter, which was utilized for the training and the input parameter for the predict and verification were 0.807 and 0.728, respectively. Based on the results, we predict that it can be utilized to estimate concentration time and analyze reliability of urban stream.

Keywords

Urban stream

Time of concentration

Oncheoncheon basin

Artificial neuron network

Matlab

하천 관리에 있어 도달시간은 중요한 인자 중의 하나이다. 특히 사회적으로 다양한 하천 활용에 대한 요구가 높아짐에 따라 친수공간으로써 하천에서의 정확한 도달시간 산정은 홍수시 주민 대피 시간 확보 등을 위해서 매우 중요하다. 그러나 과거 도달시간 산정에 대한 연구는 자연 하천의 복합 유역에서의 단일 수문사상에 대하여 연구가 수행되어왔으며, 도심하천의 단일유역을 대상으로 복합 수문 사상에 대한 도달시간 산정방법의 개발은 미흡한 실정이다. 따라서 최근 집중호우에 의하여 빈번한 침수 피해가 발생된 부산광역시 대표 도심하천인 온천천 유역에 대하여 과거 10년(2006~2015년) 동안의 강우-유출량 자료를 이용하여 도달시간을 산정하였고, Matlab 기반의 인공신경망 기법을 이용하여 신뢰성을 검토하였다. 12시간 이상 무강우를 기준으로 총 254개의 강우 사상을 분리하였고, 이를 바탕으로 총 강우량, 총 유출량, 첨두 강우량/총 강우량, 첨두 유출량/총 유출량, 지체시간, 도달시간 등 총 6개의 변수를 산정하여 인공신경망 모형의 훈련 및 검증에 활용하였다. 그 결과 훈련에 과 예측 및 검증에 활용된 입력 변수의 상관관계는 각 각 0.807 및 0.728로 나타났으며, 연구결과를 바탕으로 도심하천의 도달시간 산정결과의 신뢰성 분석에 이를 활용할 수 있을 것으로 판단된다.

키워드

도심하천

도달시간

온천천 유역

인공신경망

Matlab

References

Bae, D. H., and Kim, Y. J. (2015). “Development of concentration time and storage coefficient considering regional trend in urban stream watershed.” Journal of Korea Water Resources Association, Vol. 48, No. 6, pp. 479-489.

Jeong, D. I., Kim, Y. O., and Go, I. H. (2003). “A study in rainfall- runoff models for improving ensemble streamflow prediction -Ⅱ. Combining rainfall-runoff models-.” Journal of Korean Society Civil Engineers, Vol. 23, No. 6B, pp. 531-540.

Jeong, D. I., Kim, Y. O., Cho, S. Z., and Shin, H.J. (2003). “A study on rainfall-runoff models for improving ensemble streamflow prediction -Ⅰ. Rainfall-runoff models using artificial neural neworks-.” Journal of Korean Society Civil Engineers, Vol. 23, No. 6B, pp. 521-530.

Jeong, J. H., Keum, J. H., and Yoon, Y. N. (2002). “Development of an estimation method for travel time.” Journal of Korea Water Resources Association, Vol. 35, No. 6, pp. 479-489.

Kim, D. P., and Lee, D. Y. (2016). “A study on method of application of hydrological factors affecting the time of concentration estimation.” 2016 Korean Society Civil Engineers Regular Conference, 2016, pp. 134-135.

Kim, D. S. (2005), Neural networks: theory and applications. 1. JinhanM&B, 2005.

Kim, J. S., Choi, C. H., Kim, D. H., Bak, J. U., and Kim, H. S. (2016), “Development rain damage prediction functions using artificial neural networks.” 2016 Korean Society Civil Engineers Regular Conference, 2016, pp. 64-65.

Kirpich, Z. P. (1940), “Time of concentration of small agriculture watersheds.” Civil Engineering, Vol. 10, No. 6, pp. 362-368.

Kwak, J. W., Kim, H. S., and Kim, D. G. (2008), “A study of parameter calibrations for flood forecasting models.” 2008 Korean Society Civil Engineers Regular Conference, 2008. 10, pp. 3657-3660.

Kwon, J. H., Park, M. J, and Kim, J. H. (2004), “Rainfall analysis to estimate the amount of non-point source pollution.” Journal of Korea Water Resources Association 2004 Conference, pp. 666-670.

Lee, J. H., Kim, J. S., and Lee, J. C. (2013), “Drought forecasting using the Multi Layer Perceptron (MLP) artificial neural network model.” Journal of Korea Water Resources Association, Vol. 46, No. 12, pp. 1249-1263.

Lee, J. J., Kim, H. N., and Kwak, C. J. (2006), “An analysis of the areal and hydrologic characteristic parameters of detention pond according to urbanization.” 2006 Korean Society of Civil Engineers Regular Conference, 2006, pp. 1140-1143.

Lee, K. S, Park, S. C., Lee, H. M., and Jin, Y. H. (2000). “The study on the forecasting of runoff applied the B.P. algorithm of the artificial beural network in the Young-San river.” Journal of Korean Society Civil Engineers, Vol. 20, No. 5B, pp. 679-688.

Park, J. K., and Hwang, M. Y. (2003), “Development of rainfall-runoff process forecasting model using artificial neural network.” Journal of Korean Society of Environmental Administration, Vol. 9, No. 1, pp. 127-135.

Restrepo-Posada, P. J., and Eagleson, P. S. (1982), “Identification of independent rainstorms.” Journal of Hydrology, Vol. 55, pp. 303-319.

Shin, H. S., Kang, D. K., Jang, H. K., and Song, Y. H. (2007). “For verification the prediction of short term rainfall by using neural network method.” 2007 Korean Society of Civil Engineers Regular Conference, 2007, pp. 2678-2681.

Shon, T. S., Kang, D. H., Jang J. K., and Shin, H. S. (2010), “A study of assessment for internal inundation vulnerability in urban area using SWMM.” Journal of the Korean Society of Hazard Mitigation, Vol.10, No. 4, pp. 105-117.

Shon, T. S., Kim, M. E., Jang, Y. S., and Shin, H. S., (2012) Computation of peak flow in Oncheon basin using SWMM, 2012 Korean Society of Civil Engineers Conference, pp. 597-599.

Yoo, D. H., and Jun, W. Y. (2000). “ Time of concentration on impervious overland.” Journal of Korea Water Resources Association, Vol. 33, No. 2, pp. 195-205.

Yoon, K. H., Seo, B. C., and Shin, H. S. (2004). “Real-time forecasting of flood runoff based on neural networks in Nakdong River basin & application to flood warning system.” Journal of Korea Water Resources Association, Vol. 37, No. 2, pp. 145-154.

Yoon, T. H., Kim, S. T., and Park, J. W. (2005). “On redefining of parameters of clark model.” Journal of Korean Society Civil Engineers, Vol. 25, No. 3B, pp. 181-187.

Information

Publisher :KOREA WATER RESOURECES ASSOCIATION
Publisher(Ko) :한국수자원학회
Journal Title :Journal of Korea Water Resources Association
Journal Title(Ko) :한국수자원학회 논문집
Volume : 51
No :1
Pages :71-80
Received Date : 2017-09-21
Revised Date : 2017-11-17
Accepted Date : 2017-11-17
DOI :https://doi.org/10.3741/JKWRA.2018.51.1.71

Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

All Issue