Flood routing by linear Muskingum method using two basic floods data using particle swarm optimization (PSO) algorithm
The Muskingum method is one of hydrological approaches that has been used for flood routing for many years thanks to its simplicity and reasonable accuracy over other methods. In engineering works, the calculation of the Peak section of a flood hydrograph is crucially important. In the present study...
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| Published in | Water science & technology. Water supply Vol. 20; no. 5; pp. 1897 - 1908 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
IWA Publishing
01.08.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1606-9749 1607-0798 1607-0798 |
| DOI | 10.2166/ws.2020.099 |
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| Abstract | The Muskingum method is one of hydrological approaches that has been used for flood routing for many years thanks to its simplicity and reasonable accuracy over other methods. In engineering works, the calculation of the Peak section of a flood hydrograph is crucially important. In the present study, using the particle swarm optimization (PSO) algorithm, instead of using a single basic flood, the parameters of the linear Muskingum method (X, K, Δt) are calculated by computed arithmetic and geometric means relevant to two basic floods in the form of eight different models for calculating the downstream hydrograph. The results indicate that if the numerical values of the calculated flood inflow are placed in the interval of the inflow and the basic flood which the parameters X, K, Δt are from, the computation accuracy in approximating the outflow flood related to the peak section of the inflow hydrograph increases for all the mentioned models. In other words, if the arithmetic mean of X, K and the geometric mean of Δt, relevant to the two basic floods, are used instead of using values of X, K, Δt of a single basic flood, the computational accuracy in estimating the flood peak section of the hydrograph in downstream has the highest increase among all the eight models. Thus, the Mean Relative Error (MRE) relevant to the peak section of the inflow hydrograph of the third flood (observational flood) obtained by the first and second basic floods was equal to 4.89% and 2.91%, respectively, while in case of using the arithmetic mean of X and K and the geometric mean of Δt, related to the first and second basic floods (the best models presented in this study), this value is equal to 1.66%. |
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| AbstractList | The Muskingum method is one of hydrological approaches that has been used for flood routing for many years thanks to its simplicity and reasonable accuracy over other methods. In engineering works, the calculation of the Peak section of a flood hydrograph is crucially important. In the present study, using the particle swarm optimization (PSO) algorithm, instead of using a single basic flood, the parameters of the linear Muskingum method (X, K, Δt) are calculated by computed arithmetic and geometric means relevant to two basic floods in the form of eight different models for calculating the downstream hydrograph. The results indicate that if the numerical values of the calculated flood inflow are placed in the interval of the inflow and the basic flood which the parameters X, K, Δt are from, the computation accuracy in approximating the outflow flood related to the peak section of the inflow hydrograph increases for all the mentioned models. In other words, if the arithmetic mean of X, K and the geometric mean of Δt, relevant to the two basic floods, are used instead of using values of X, K, Δt of a single basic flood, the computational accuracy in estimating the flood peak section of the hydrograph in downstream has the highest increase among all the eight models. Thus, the Mean Relative Error (MRE) relevant to the peak section of the inflow hydrograph of the third flood (observational flood) obtained by the first and second basic floods was equal to 4.89% and 2.91%, respectively, while in case of using the arithmetic mean of X and K and the geometric mean of Δt, related to the first and second basic floods (the best models presented in this study), this value is equal to 1.66%. |
| Author | Bazargan, Jalal Norouzi, Hadi |
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| Cites_doi | 10.1016/j.jhydrol.2015.09.028 10.1061/(ASCE)0733-9429(1997)123:2(137) 10.2166/nh.2016.089 10.1007/s11269-018-2082-6 10.1061/(ASCE)1084-0699(2002)7:6(413) 10.1061/(ASCE)0733-9437(2004)130:2(140) 10.1007/s11269-011-9829-7 10.1016/j.jhydrol.2004.10.027 10.1061/(ASCE)HE.1943-5584.0000379 10.1016/j.jhydrol.2011.01.017 10.2166/nh.2016.185 10.1016/j.jhydrol.2017.05.024 10.1023/A:1020274409612 10.1007/s12205-013-0037-2 10.1016/j.jhydrol.2015.01.068 10.1061/(ASCE)HE.1943-5584.0000070 10.1007/s11269-016-1278-x 10.1080/19942060.2018.1448896 10.1061/(ASCE)HE.1943-5584.0001122 10.3390/w10091130 10.2166/nh.2017.251 10.1007/s11269-007-9229-1 10.1016/j.jhydrol.2016.10.030 10.1061/(ASCE)0733-9496(2007)133:3(192) 10.1061/(ASCE)HE.1943-5584.0001603 |
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| StartPage | 1897 |
| SubjectTerms | Accuracy Algorithms Arithmetic Computation Computer applications Downstream effects Flood hydrographs Flood peak Flood routing Floods Genetic algorithms Hydraulics Hydrology Inflow Mathematical models Methods Optimization Outflow Parameters Particle swarm optimization River networks Routing |
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| Title | Flood routing by linear Muskingum method using two basic floods data using particle swarm optimization (PSO) algorithm |
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