Development and Application of Advanced Muskingum Flood Routing Model Considering Continuous Flow

被引:15
作者
Lee, Eui Hoon [1 ]
Lee, Ho Min [2 ]
Kim, Joong Hoon [2 ]
机构
[1] Korea Univ, Res Ctr Disaster Prevent Sci & Technol, Seoul 02841, South Korea
[2] Korea Univ, Sch Civil Environm & Architectural Engn, Seoul 02841, South Korea
基金
新加坡国家研究基金会;
关键词
advanced nonlinear Muskingum model; flood routing; continuous flow; vision correction algorithm; PARAMETER-ESTIMATION;
D O I
10.3390/w10060760
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The Muskingum flood routing model is a representative flood routing model. The field applicability of the Muskingum flood routing model is known to be good, and the structure of input data is simple. However, accurate flood routing cannot be conducted using current Muskingum flooding routing models due to the structural limitation of equations. The advanced nonlinear Muskingum flood routing model is suggested for improving accuracy, considering continuous flow using weighted inflow. Continuous flow means the past continuous inflows, including first and secondary inflow over time. Five flood data were selected for a comparison between the results of this study and previous ones. The sum of squares, root mean square errors, and Nash-Sutcliffe efficiency are applied in order to calculate the error values. The vision correction algorithm was used to estimate parameters in the new model. Generally, the new method yields better results than those described in previous studies, though it shows similar results with the most recent methods (NLMM-L) in some flood data. Finally, the new method and NLMM-L are applied for the prediction of Daechung flood data in Korea. The new method is useful in the prediction of outflows, because it shows better results than NLMM-L.
引用
收藏
页数:21
相关论文
共 35 条
[31]   Development of predictive model for flood routing using genetic expression programming [J].
Bagatur, T. ;
Onen, F. .
JOURNAL OF FLOOD RISK MANAGEMENT, 2018, 11 :S444-S454
[32]   Flood routing using the Muskingum model based on data clustering approaches and the Bald Eagle Search Optimization algorithm [J].
Haiati, Farshad ;
Yaghoubi, Behrouz ;
Nazif, Sara .
MODELING EARTH SYSTEMS AND ENVIRONMENT, 2025, 11 (04)
[33]   Application of Feedforward Artificial Neural Network in Muskingum Flood Routing: a Black-Box Forecasting Approach for a Natural River System [J].
Zaw Zaw Latt .
Water Resources Management, 2015, 29 :4995-5014
[34]   Application of MIKE21 MODEL on Flood Routing in the Lower Yellow River from Gaocun to Sunkou [J].
Zhang, Xiaolei ;
Guo, Shaolei .
INNOVATION IN CIVIL ENGINEERING, ARCHITECTURE AND SUSTAINABLE INFRASTRUCTURE, 2012, 238 :304-307
[35]   A dynamic non-isothermal model for a hydrocracking reactor: Model development by the method of continuous lumping and application to an industrial unit [J].
Sildir, Hasan ;
Arkun, Yaman ;
Cakal, Berna ;
Gokce, Dila ;
Kuzu, Emre .
JOURNAL OF PROCESS CONTROL, 2012, 22 (10) :1956-1965