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http://dx.doi.org/10.14346/JKOSOS.2020.35.2.28

Prediction of Water Level at Downstream Site by Using Water Level Data at Upstream Gaging Station  

Hong, Won Pyo (Department of Safety Engineering, Incheon National University)
Song, Chang Geun (Department of Safety Engineering, Incheon National University)
Publication Information
Journal of the Korean Society of Safety / v.35, no.2, 2020 , pp. 28-33 More about this Journal
Abstract
Recently, the overseas construction market has been actively promoted for about 10 years, and overseas dam construction has been continuously performed. For the economic and safe construction of the dam, it is important to prepare the main dam construction plan considering the design frequency of the diversion tunnel and the cofferdam. In this respect, the prediction of river level during the rainy season is significant. Since most of the overseas dam construction sites are located in areas with poor infrastructure, the most efficient and economic method to predict the water level in dam construction is to use the upstream water level. In this study, a linear regression model, which is one of the simplest statistical methods, was proposed and examined to predict the downstream level from the upstream level. The Pyeongchang River basin, which has the characteristics of the upper stream (mountain stream), was selected as the target site and the observed water level in Pyeongchang and Panwoon gaging station were used. A regression equation was developed using the water level data set from August 22th to 27th, 2017, and its applicability was tested using the water level data set from August 28th to September 1st, 2018. The dependent variable was selected as the "level difference between two stations," and the independent variable was selected as "the level of water level in Pyeongchang station two hours ago" and the "water level change rate in Pyeongchang station (m/hr)". In addition, the accuracy of the developed equation was checked by using the regression statistics of Root Mean Square Error (RMSE), Adjusted Coefficient of Determination (ACD), and Nach Sutcliffe efficiency Coefficient (NSEC). As a result, the statistical value of the linear regression model was very high, so the downstream water level prediction using the upstream water level was examined in a highly reliable way. In addition, the results of the application of the water level change rate (m/hr) to the regression equation show that although the increase of the statistical value is not large, it is effective to reduce the water level error in the rapid level rise section. Accordingly, this is a significant advantage in estimating the evacuation water level during main dam construction to secure safety in construction site.
Keywords
water level estimation; regression equation; upstream water level; regression statistics;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 MOLIT, Dam Desgin Criteria. 2011.
2 S. J. Cho, S. W. Shin, S. H. Sim and J. Y. Lim, "Failure Probability Assessment for Risk Analysis of Concrete Dam Under Flood", J. Korean Soc. Saf., Vol. 31, No. 6, pp. 58-66, 2016.   DOI
3 S. R. Moon, S. M. Yang and S. H. Choi, "Development and the Application of Flood Disaster Risk Reduction Index", J. Korean Soc. Saf., Vol. 29, No. 1, pp. 64-69, 2014.   DOI
4 T. N. Keeper, "Comparison of Linear Systems and Finite Difference Flow Routing Techinques", Water Resources Research, Vol. 12, Issue 5, pp. 997-1006, 1976.   DOI
5 M. W. Son and K. S. Lee, "Forecasting of Flood Stage Using Neural Networks and Regression Analysis.", Journal of Korean Society of Civil Engineers , Vol. 23, No. 3B, pp. 147-155, 2003.
6 H. N. Phien and N. D. A. Kha, "Flood Forecasting for the Upper Reach of the Red River Basin North Vietnam", Water Research Commission SA, Vol. 29, No. 3, pp. 267-272, 2003.
7 S. C. Park, Y. Lee, Y. H. Jin and C. Y. Oh, "A Water Level Forecasting among Upstream and Downstream Points by Using Neural Network Algorithms.", Journal of Korean Society of Water Science and Technology, Vol. 13, No. 3, pp. 45-54, 2005.
8 H. D. Jeon, J. H. Lee, and M. J. Park, "A Methodology for Flood Forecasting and Warning Based on the Characteristic of Observed Water Levels Between Upstream and Downstream", Journal of the Korean Society of Hazard Mitigation, Vol. 13, No. 6, pp. 367-374, 2013.   DOI
9 B. J. Kim, "Comparative Study of Storage Function and SSARR Models for the Flood Hydrograph Forecasting of a Miho Stream", Inha University, 2007.
10 S. Y. Choi, K. Y. Han and B. H. Kim, "Comparison of Different Multiple Linear Regression Models for Real-time Flood Stage Forecasting", Journal of Korean Society of Civil Engineers. Vol. 32, No. 1, pp. 9-20, 2012.
11 S. J. Byeon, S. H. Lee, G. W. Choi and K. J. Jung, "Use of Gauged Water Level and Precipitation Data to Predict Short Term Water Level Changes", Korean Review of Crisis and Emergency Management, Vol. 10, No. 1, pp. 247-264, 2014.
12 Kangwon Province, Pyeongchang River Basic Plan (changed) Report, 2012.