Parameter Estimation of Extended Nonlinear Muskingum Models with the Weed Optimization Algorithm

Hamedi, Farzan; Bozorg‐Haddad, Omid; Pazoki, Maryam; Asgari, Hamidreza; Parsa, Mehran; Loáiciga, Hugo A.

doi:10.1061/(asce)ir.1943-4774.0001095

Cited by 28 publications

(14 citation statements)

References 58 publications

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“…Two case studies were considered as bench problems, which have been used by different researchers using many methods for flood routing (Wilson and Karahan floods), and one case study was related to a river in Myanmar that had an important flood. The Wilson flood is considered as an important case study and different researchers tested different algorithms on this case study [2,3,[28][29][30], and thus a comprehensive study can be considered for this case study. The Karahan flood is considered as one of the case studies that have been investigated by different researchers as a benchmark problem [28][29][30][31]35].…”

Section: Resultsmentioning

confidence: 99%

“…Flood routing is fundamental to the design of structural, as well as nonstructural, flood control measures [1]. Routing involves the calculation of changes in the magnitude, velocity, and shape of a flood wave, as a function of time at one or several points of the river [2]. There are two types of flood routing methods: Hydraulic and hydrological.…”

Section: Introductionmentioning

confidence: 99%

“…Using an Invasive Weed Optimization Algorithm (IWOA) for parameter optimization, Hamedi et al [2] found the five-parameter Muskingum method to be more accurate than the four-parameter version. With PSO for parameter estimation, Moghadam et al [30] found the fourparameter Muskingum method to be more accurate, than the three-parameter Muskingum method with GA and linear programming.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm

Farzin

Singh

Karami

et al. 2018

Water

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Design of hydraulic structures, flood warning systems, evacuation measures, and traffic management require river flood routing. A common hydrologic method of flood routing is the Muskingum method. The present study attempted to develop a three-parameter Muskingum model considering lateral flow for flood routing, coupling with a new optimization algorithm namely, Improved Bat Algorithm (IBA). The major function of the IBA is to optimize the estimated value of the three-parameters associated with the Muskingum model. The IBA acts based on the chaos search tool, which mainly enhances the uniformity and erogidicty of the population. In addition, the current research, unlike the other existing models which consider flood routing, is based on dividing one reach to a few intervals to increase the accuracy of flood routing models. Three case studies with lateral flow were considered for this study, including the Wilson flood, Karahan flood, and Myanmar flood. Seven performance indexes were examined to evaluate the performance of the proposed Muskingum model integrated with IBA, with other models that were also based on the Muskingum Model with three-parameters but utilized different optimization algorithms. The results for the Wilson flood showed that the proposed model could reduce the Sum of Squared Deviations (SSD) value by 89%, 51%, 93%, 69%, and 88%, compared to the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Pattern Search (PS) algorithm, Harmony Search (HS) algorithm, and Honey Bee Mating Optimization (HBMO), respectively. In addition, increasing the number of intervals for flood routing significantly improved the accuracy of the results. The results indicated that the Sum of Absolute Deviations (SAD) using IBA for the Karahan flood was 117, which had reduced by 83%, 88%, 94%, and 12%, compared to the PSO, GA, HS, and BA, respectively. Furthermore, the achieved results for the Myanmar flood showed that SSD for IBA relative to GA, BA, and PSO was reduced by 32%, 11%, and 42%, respectively. In conclusion, the proposed Muskingum Model integrated with IBA considering the existence of lateral flow, outperformed the existing applied simple Muskingum models in previous studies. In addition, the more the number of intervals used in the model, the better the accuracy of flood routing prediction achieved.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm

Farzin

Singh

Karami

et al. 2018

Water

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“…As its modest data requirements make it attractive for practical use, the Muskingum method is a popular alternative for routing flood waves through stream reaches. Therefore, various versions of the method, especially methods for estimating its parameters, are well established in the hydrological literature (e.g., McCarthy, 1938;Ponce and Yevjevich, 1978;Ponce and Theurer, 1982;Ponce and Chaganti, 1994;O'Donnell et al, 1998;Tang, et al, 1999;Birkhead and James, 2002;Al-Humoud and Esen, 2006;Perumal and Sahoo, 2008;Easa, 2013;Karahan et al, 2015;Niazkar et al, 2016;Afzali, 2016;Hamedi et al, 2016;Chulsang et al, 2017;Oliveira et al, n.d.;Asgari et al, 2018;Bai et al, 2018;Farzin et al, 2018;Kang and Zhou 2018;Meyer et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Variable Parameter Multilinear Muskingum Method: Case Study on the Danube River

Danáčová

Szolgay

2018

Slovak Journal of Civil Engineering

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The Muskingum method is based on a linear relationship between a channel’s storage and inflow and outflow discharges. The applicability of using travel-time discharge relationships to model the variability of the K parameter in a Muskingum routing model was tested. The new parameter estimation method is based on the relationships between the traveltime parameter (K) and the input discharge for the reach of the Danube River between Devín-Bratislava and Medveďov, which includes the Gabčíkovo hydropower scheme. The variable parametrisation method was compared with the classical approach. The parameter X was taken as the average of its values from a small set of flood waves, K was estimated as a function of the travel-time parameter and discharge, which was optimized for one flood wave. The results were validated using the Nash-Sutcliffe coefficient on 5 floods. The results obtained by these methods were satisfactory and, with their use, one could reduce the amount of data required for calibration in practical applications.

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“…Publications that play the up-the-wall game are too many to list. Papers by Asgari et al (2015), Azizipour et al (2016), and Hamedi et al (2016), the original paper, and Garousi-Nejad et al (2016) are some recent examples of research that plays the up-the-wall game without bringing any contribution to water resources engineering. The water resources community is invited to drop and ban this detrimental direction of research.…”

mentioning

confidence: 99%

Discussion of “Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production” by Irene Garousi-Nejad, Omid Bozorg-Haddad, Hugo A. Loáiciga, and Miguel A. Mariño

Moravej

2018

J. Irrig. Drain Eng.

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In the original paper, the authors made an effort to show the application of the firefly algorithm (FA) and its superiority to solve different reservoir operation problems in comparison with the genetic algorithm (GA). Although the paper seems interesting, the discusser would like to mention following points for future studies.The authors claimed that the FA has not been applied for the optimal operation of reservoir systems before. On the one hand, Hosseini-Moghari and Banihabib (2014) applied the FA to optimize the operation of the Bazoft Reservoir. Furthermore, there are numerous different kinds of lifeform throughout the world; this cannot mean that researchers should develop, modify, and apply almost infinite different algorithms based on insects, fish, mammals, and plants. Over the last decade, there has been an explosion in the development of new so-called metaheuristic optimization algorithms based on natural metaphors. These algorithms have attracted criticism globally (Weyland

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Parameter Estimation of Extended Nonlinear Muskingum Models with the Weed Optimization Algorithm

Cited by 28 publications

References 58 publications

Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm

Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm

Variable Parameter Multilinear Muskingum Method: Case Study on the Danube River

Discussion of “Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production” by Irene Garousi-Nejad, Omid Bozorg-Haddad, Hugo A. Loáiciga, and Miguel A. Mariño

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