Discussion: New and improved four-parameter non-linear Muskingum model

Easa, Said M.; Barati, Reza; Shahheydari, Hossein; Nodoshan, Ehsan Jafari; Barati, Tahereh

doi:10.1680/wama.14.00030

Cited by 19 publications

(12 citation statements)

References 7 publications

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“…Easa (2014b) a This model, based on 5P-NLMM by Easa et al (2014), preserves the physical significance of the original parameters. The model assumes that the characteristics of the upstream and downstream sections are not constant.…”

Section: Referencementioning

confidence: 99%

“…For this reason, an alternative strategy has been adopted by the author to improve the performance of the model: modifying its structure instead of its solution algorithm. This effort has initially produced two new models with constant parameters, 4P-NLMM and 6P-NLMM (Easa, 2014a), where the latter model is based on Barati et al's contribution in Easa et al (2014).…”

Section: Introductionmentioning

confidence: 99%

“…Muskingum models with variable parameters have also been recently proposed (Table 1), including two models with a variable exponent parameter (VEP), NLMM-VEP and 5P-NLMM-VEP, and a model with four variable parameters, 8P-NLMM-VP (Easa, 2013, 2014band Easa et al, 2014. The four-variable-parameter model is general, and produces 15 special models from which models with four to six parameters have been recommended for different types of hydrographs.…”

Section: Introductionmentioning

confidence: 99%

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Multi-criteria optimisation of the Muskingum flood model: a new approach

Easa¹

2015

Proceedings of the Institution of Civil Engineers - Water Manag

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Existing Muskingum hydrological routing models adopt a single criterion in the calibration process. Some models minimise the sum of the squared deviations between estimated and observed outflows (outflow criterion), while others minimise the sum of the squared deviations between the estimated and observed storages (storage criterion). However, models that adopt the outflow (storage) criterion result in a poor fit to the observed storages (outflows). This paper presents a new approach that incorporates both criteria in the calibration process and aids trade-off analysis. The multi-criteria function is expressed as a weighted function of normalised outflow and storage criteria, representing the deviations from ideal outflow and storage values. The routing procedure is based on the author's four-parameter Muskingum model with constant parameters and the fourth-order Runge-Kutta method. The proposed model was applied to three examples. A criterion weight of 0 . 4-0 . 6 was found to produce an excellent trade-off between outflow and storage criteria. The results show that the model substantially improves on both criteria compared with single-criterion models. The proposed model, which properly captures the entire flood propagating characteristics in calibration, should be of interest to hydrological engineers and practitioners.

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Section: Referencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-criteria optimisation of the Muskingum flood model: a new approach

Easa¹

2015

Proceedings of the Institution of Civil Engineers - Water Manag

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“…Easa [20] introduced a four-parameter nonlinear Muskingum model for flood routing and showed that the four-parameter model estimated flood discharge better than the three-parameter model did. The GA was used to obtain the optimal values of the Muskingum parameters.…”

Section: Introductionmentioning

confidence: 99%

Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

Ehteram

Othman

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2018

Water

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Flood prediction and control are among the major tools for decision makers and water resources planners to avoid flood disasters. The Muskingum model is one of the most widely used methods for flood routing prediction. The Muskingum model contains four parameters that must be determined for accurate flood routing. In this context, an optimization process that self-searches for the optimal values of these four parameters might improve the traditional Muskingum model. In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. Data for the three different case studies from the USA and the UK were utilized to examine the suitability of the proposed HBSA for flood routing. Comparative analyses based on the sum of squared deviations (SSD), sum of absolute deviations (SAD), error of peak discharge, and error of time to peak showed that the proposed HBSA based on the Muskingum model achieved excellent flood routing accuracy compared to that of other methods while requiring less computational time.

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“…This themed issue also includes one discussion submission (Easa et al (2014)) on an earlier paper, on proposed improvements to the non-linear Muskingum hydrological flood routing method. This item is not a direct contribution to the themed issue, but it is welcome, as flood routing is relevant to operators of impounding reservoirs.…”

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confidence: 99%

Editorial

Hamilton¹

2014

Proceedings of the Institution of Civil Engineers - Water Manag

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Discussion: New and improved four-parameter non-linear Muskingum model

Cited by 19 publications

References 7 publications

Multi-criteria optimisation of the Muskingum flood model: a new approach

Multi-criteria optimisation of the Muskingum flood model: a new approach

Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

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