Effects of uncertainty in determining the parameters of the linear Muskingum method using the particle swarm optimization (PSO) algorithm

The Muskingum method is one the simplest and most applicable methods of flood routing. Optimizing the coefficients of linear Muskingum is of great importance to enhance accuracy of computations on an outflow hydrograph. In this study, considering the uncertainty of flood in the rivers and by applica...

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Autores principales:	Hadi Norouzi, Jalal Bazargan
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	basic flood linear muskingum method parameters optimization particle swarm optimization (pso) algorithm uncertainty Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/05f719d3324b46ec8e93a826e192c460
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spelling	oai:doaj.org-article:05f719d3324b46ec8e93a826e192c4602021-11-05T19:02:24ZEffects of uncertainty in determining the parameters of the linear Muskingum method using the particle swarm optimization (PSO) algorithm2040-22442408-935410.2166/wcc.2021.227https://doaj.org/article/05f719d3324b46ec8e93a826e192c4602021-08-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/5/2055https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354The Muskingum method is one the simplest and most applicable methods of flood routing. Optimizing the coefficients of linear Muskingum is of great importance to enhance accuracy of computations on an outflow hydrograph. In this study, considering the uncertainty of flood in the rivers and by application of the particle swarm optimization (PSO) algorithm, we used the data obtained from three floods simultaneously as basic flood to optimize parameters of linear Muskingum (X, K and ), rather than using inflow and outflow hydrographs of a single basic flood (observational flood), and optimized the outflow discharge at the beginning of flood (O1) as a percentage of inflow discharge at the beginning of flood (I1). The results suggest that the closer inflow discharge variation of basic flood to the inflow discharge variation of observational flood, the greater the accuracy of outflow hydrograph computations. Moreover, when the proposed approach is used to optimize parameters of X, K and , the accuracy of outflow hydrograph computations will increase too. In other words, if rather than using a single basic flood, the proposed approach is applied, the average values of mean relative error (MRE) of total flood for the first, second, third and fourth flood will be improved as 31, 13, 39 and 33%, respectively. HIGHLIGHTS Optimization of the parameters of the linear Muskingum method.; Using the PSO algorithm to optimize the parameters.; To consider the uncertainty of the flood.; Considering the inflow and outflow hydrographs of three floods simultaneously to the basic flood.; Optimizing the outflow discharge at the start of the flood (O1) as a fraction of the inflow discharge at the start of the flood (I1).;Hadi NorouziJalal BazarganIWA Publishingarticlebasic floodlinear muskingum methodparameters optimizationparticle swarm optimization (pso) algorithmuncertaintyEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 5, Pp 2055-2067 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	basic flood linear muskingum method parameters optimization particle swarm optimization (pso) algorithm uncertainty Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	basic flood linear muskingum method parameters optimization particle swarm optimization (pso) algorithm uncertainty Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Hadi Norouzi Jalal Bazargan Effects of uncertainty in determining the parameters of the linear Muskingum method using the particle swarm optimization (PSO) algorithm
description	The Muskingum method is one the simplest and most applicable methods of flood routing. Optimizing the coefficients of linear Muskingum is of great importance to enhance accuracy of computations on an outflow hydrograph. In this study, considering the uncertainty of flood in the rivers and by application of the particle swarm optimization (PSO) algorithm, we used the data obtained from three floods simultaneously as basic flood to optimize parameters of linear Muskingum (X, K and ), rather than using inflow and outflow hydrographs of a single basic flood (observational flood), and optimized the outflow discharge at the beginning of flood (O1) as a percentage of inflow discharge at the beginning of flood (I1). The results suggest that the closer inflow discharge variation of basic flood to the inflow discharge variation of observational flood, the greater the accuracy of outflow hydrograph computations. Moreover, when the proposed approach is used to optimize parameters of X, K and , the accuracy of outflow hydrograph computations will increase too. In other words, if rather than using a single basic flood, the proposed approach is applied, the average values of mean relative error (MRE) of total flood for the first, second, third and fourth flood will be improved as 31, 13, 39 and 33%, respectively. HIGHLIGHTS Optimization of the parameters of the linear Muskingum method.; Using the PSO algorithm to optimize the parameters.; To consider the uncertainty of the flood.; Considering the inflow and outflow hydrographs of three floods simultaneously to the basic flood.; Optimizing the outflow discharge at the start of the flood (O1) as a fraction of the inflow discharge at the start of the flood (I1).;
format	article
author	Hadi Norouzi Jalal Bazargan
author_facet	Hadi Norouzi Jalal Bazargan
author_sort	Hadi Norouzi
title	Effects of uncertainty in determining the parameters of the linear Muskingum method using the particle swarm optimization (PSO) algorithm
title_short	Effects of uncertainty in determining the parameters of the linear Muskingum method using the particle swarm optimization (PSO) algorithm
title_full	Effects of uncertainty in determining the parameters of the linear Muskingum method using the particle swarm optimization (PSO) algorithm
title_fullStr	Effects of uncertainty in determining the parameters of the linear Muskingum method using the particle swarm optimization (PSO) algorithm
title_full_unstemmed	Effects of uncertainty in determining the parameters of the linear Muskingum method using the particle swarm optimization (PSO) algorithm
title_sort	effects of uncertainty in determining the parameters of the linear muskingum method using the particle swarm optimization (pso) algorithm
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/05f719d3324b46ec8e93a826e192c460
work_keys_str_mv	AT hadinorouzi effectsofuncertaintyindeterminingtheparametersofthelinearmuskingummethodusingtheparticleswarmoptimizationpsoalgorithm AT jalalbazargan effectsofuncertaintyindeterminingtheparametersofthelinearmuskingummethodusingtheparticleswarmoptimizationpsoalgorithm
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Effects of uncertainty in determining the parameters of the linear Muskingum method using the particle swarm optimization (PSO) algorithm

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