A new nonlinear Muskingum flood routing model incorporating lateral flow

Karahan, Halil; Gürarslan, Gürhan; Geem, Zong Woo

doi:10.1080/0305215x.2014.918115

Cited by 49 publications

(60 citation statements)

References 26 publications

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“…Spatially distributed modeling is a typical example of the mechanistic approach to construct a model that explicitly accounts for as much of the small-scale physics and the natural heterogeneity as computationally possible by considering variables such as groundwater, precipitation, evapotranspiration, initial soil moisture content and temperature (Loague and VanderKwaak, 2004). A large number of studies have examined the phenomenon of flood wave movement in rivers and channels using physically based methods (O'Donnell, 1985;Perumal et al, 2001;Madsen and Skotner, 2005;Chen and Yu, 2007;Romanowicz et al 2006;Karahan et al, 2014;Yadav et al, 2015). Although physical methods provide reasonable accuracy, their implementation and calibration typically present various difficulties (Nayak et.…”

Section: Introductionmentioning

confidence: 99%

Discharge forecasting using an Online Sequential Extreme Learning Machine (OS-ELM) model: A case study in Neckar River, Germany

Yadav

Mathur

et al. 2016

Measurement

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Section: Introductionmentioning

confidence: 99%

Discharge forecasting using an Online Sequential Extreme Learning Machine (OS-ELM) model: A case study in Neckar River, Germany

Yadav

Mathur

et al. 2016

Measurement

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“…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. In addition, a few Muskingum models with a lateral inflow parameter have been proposed in the literature, and include 3P-LMM-L by O'Donnell (1985), 4P-NLMM-L model by Karahan et al (2014), and 6P-NLMM-VWP-L with variable weighting parameter (VWP) by Easa (2014b). The multi-criteria approach proposed in this paper can be implemented with any of the storage models of where dS/dt is the rate of change of the storage volume with respect to time t, and S, I and Q are the simultaneous amounts of storage, inflow and outflow, respectively.…”

Section: Introductionmentioning

confidence: 99%

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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“…Additionally, basing PSO and CSS on more population diversity increased the convergence speed of the algorithm. A nonlinear Muskingum model that considers lateral flow was used for flood routing [24]. This model used the cuckoo algorithm and predicted peak discharge more accurately than other models.…”

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.

show abstract

A new nonlinear Muskingum flood routing model incorporating lateral flow

Cited by 49 publications

References 26 publications

Discharge forecasting using an Online Sequential Extreme Learning Machine (OS-ELM) model: A case study in Neckar River, Germany

Discharge forecasting using an Online Sequential Extreme Learning Machine (OS-ELM) model: A case study in Neckar River, Germany

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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