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RTK 방법 및 회귀분석 방법을 이용한 RDII 예측 결과 비교

Comparisons of RDII Predictions Using the RTK-based and Regression Methods

  • 김정률 (중앙대학교 사회기반시스템공학부) ;
  • 이재현 (중앙대학교 사회기반시스템공학부) ;
  • 오재일 (중앙대학교 사회기반시스템공학부)
  • Kim, Jungruyl (Department of Civil and Environmental Engineering, Urban Design and studies, Chung-Ang University) ;
  • Lee, Jaehyun (Department of Civil and Environmental Engineering, Urban Design and studies, Chung-Ang University) ;
  • Oh, Jeill (Department of Civil and Environmental Engineering, Urban Design and studies, Chung-Ang University)
  • 투고 : 2016.01.20
  • 심사 : 2016.03.21
  • 발행 : 2016.04.15

초록

In this study, the RDII predictions were compared using two methodologies, i.e., the RTK-based and regression methods. Long-term (1/1/2011~12/31/2011) monitoring data, which consists of 10-min interval streamflow and the amount of precipitation, were collected at the domestic study area (1.36 km2 located in H county), and used for the construction of the RDII prediction models. The RTK method employs super position of tri-triangles, and each triangle (called, unit hydrograph) is defined by three parameters (i.e., R, T and K) determined/optimized using Genetic Algorithm (GA). In regression method, the MovingAverage (MA) filtering was used for data processing. Accuracies of RDII predictions from these two approaches were evaluated by comparing the root mean square error (RMSE) values from each model, in which the values were calculated to 320.613 (RTK method) and 420.653 (regression method), respectively. As a results, the RTK method was found to be more suitable for RDII prediction during extreme rainfall event, than the regression method.

키워드

참고문헌

  1. EPA. (2005). Guide for Evaluating Capacity, Management, Operation, and Maintenance (CMOM) Programs at Sanitary Sewer Collection Systems, EPA/305/B-05/002, pp.1.1-2.7
  2. EPA. (2008). Review of Sewer Design Criteria and RDII Prediction Methods, EPA/600/R-08/010, pp.1-18.
  3. EPA. (2012). SSOAP Toolbox Enhancement and Case Study, EPA/600/R-12/690, pp.22-41.
  4. EPA. (2014). Guide for Estimating Infiltration and Inflow, http://www3.epa.gov/region1/sso/toolbox.html (November 18, 2015).
  5. Hyun, I. H., Kim, I. S., Ministry of Environment, Republic of Korea, (2008). Research on Determination Method and Application for Sewer Infiltration/Inflow of Domestic Situation
  6. Kim, H. J. (2011). Statistical Evaluation of Infiltration/Inflow in Sewers, Ph.D. Dissertation, Chung-Ang University, pp. 73-97.
  7. Ryu, J. N., Lee, J. H., and Oh, J. I. (2014). Examination of the storage function of intercepting sewers using long-term flow monitoring data, Desalination and Water Treatment, 54(4-5), 1299-1307.
  8. Vallabhaneni, S., Lai, F., Chan, C., Edward, H. B., and Richard, F. (2008). "SSOAP - A USEPA Toolbox for SSO Analysis and Control Planning", World Environmental and Water Resources Congress 2008, Ahupua'a, Hawaii.
  9. Vallabhaneni, S., and Smith, C. D. M. (2014). "Focused Field Investigations for Sewer Condition Assessment with EPA SSOAP Toolbox", North American Society for Trenchless Technology 2014 No-Dig Show, Orlando, Florida.
  10. Yoon, Y. N. (2007). Hydrology - Basic and Application. 1st Ed., Chung Moon Gak, Seoul. 435-438.
  11. Wright, L., Dent, S., Mosley, C., and Kadota, P. (2001). Comparing Rainfall Dependent Inflow and Infiltration Simulation Methods, Journal of Water Management Modeling, 207(16), 235-257.
  12. Zhang, Z. (2007). Estimating Rain Derived Inflow and Infiltration for Rainfalls of Varying Characteristics, Journal of Hydraulic Engineering, 133(1), 98-105 https://doi.org/10.1061/(ASCE)0733-9429(2007)133:1(98)