Prediction of longitudinal dispersion coefficient using laboratory and field data: relationship comparisons

Ahmad, Z.

doi:10.2166/nh.2012.047

Cited by 22 publications

(3 citation statements)

References 19 publications

(65 reference statements)

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“…The error ΔΨ in output is fundamentally the deviation sensitivity with ΔΩ being the error. The relative sensitivity can be expressed ¥ = (ΩΔΨ)/(ΨΔΩ) (Ahmad, 2013).…”

Section: Sensitivity Analysismentioning

confidence: 99%

Experimental assessment and prediction of temporal scour depth around a spur dike

Pandey

Valyrakis

et al. 2021

International Journal of Sediment Research

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Spur dikes are river training structures that have been extensively used worldwide for towards enhancing flood control and the stability of embankments and riverbanks.However, scour around spur dikes can be a major problem affecting their stability and hydraulic performance. The precise computation of temporal scour depth at spur dikes is very important for the design of economical and safe spur dikes. This study focuses on experimentally assessing the temporal variation of scour depth around a vertical wall spur dike and identifying the parameters, which mostly influence spur dike performance for a channel bed surface comprised of sand-gravel mixtures. In the current study, the authors did physical experiments in a flume based study to obtain new data, aimed at deriving a new predictive model for spur dike scour and comparing its performance to others found in the literature. It was found that the dimensionless temporal scour depth variation increases with an increase in (i) the threshold velocity ratio, (ii) the densimetric Froude number of the bed surface sediment mixture, (iii) the flow shallowness (defined as the ratio of the approach flow depth, y, to the spur dike's transverse length, l), and (iv) the flow depth-particle size ratio. It is also concluded that the temporal scour depth variation in the sediment mixture is influenced by the non-uniformity of sediment and decreases with an increase in the non-uniformity of the sediment mixture. A new mathematical model is derived for the estimation of temporal scour depths in sand-gravel sediment mixtures. The proposed equation has been calibrated and validated with the experimental data, demonstrating a good predictive capacity for the estimation of temporal scour depth evolution.

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“…The error ΔΨ in output is fundamentally the deviation sensitivity with ΔΩ being the error. The relative sensitivity can be expressed ¥ = (ΩΔΨ)/(ΨΔΩ) (Ahmad, 2013).…”

Section: Sensitivity Analysismentioning

confidence: 99%

Experimental assessment and prediction of temporal scour depth around a spur dike

Pandey

Valyrakis

et al. 2021

International Journal of Sediment Research

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“…The error ∆Ŷ in output is fundamentally the deviation sensitivity with ∆χ being the error. The relative sensitivity can be expressed ω = (χ.∆Ŷ)/(Ŷ.∆χ) [21].…”

Section: Sensitivity Analysismentioning

confidence: 99%

Scour around Spur Dike in Sand–Gravel Mixture Bed

Pandey

Lam

Cui

et al. 2019

Water

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Scour is the main cause of failure for spur dike. The accurate prediction of scour around spur dike is essential to design a spur dike. The present study focuses on the maximum scour depth in equilibrium condition and parameters, which influence it in a sand–gravel mixture bed. Outcomes of the present experimental study showed that the non-dimensional maximum equilibrium scour depth increases with critical velocity ratio (U/Uca), water depth-armour particle ratio (h/da), Froude number for sediment mixture (Frsm), water depth-spur dike length ratio (h/l), and decreases with increase in armour particle-spur dike length ratio (da/l). The maximum scour depth is proportional to dimensionless parameters of U/Uca, h/da, Frsm, h/l, but the scour depth is inverse proportional to da/l. Scour around spur dike in a sand–gravel mixture is mainly influenced by the property of the sediment mixture. The scour increases with decrease in non-uniformity of the sediment mixture. A non-linear empirical equation is proposed to estimate the maximum scour depth at an upstream nose of rectangular spur dike with a maximum error of 15%. The sensitivity analysis indicates that the maximum non-dimensional equilibrium scour depth depends on Frsm, followed by the secondary sensible parameters da/l, h/l, and h/da.

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“…For training and testing, it requires data points. In this study, the data from 30 streams was collected from the open-source literature(Deng et al 2001;Kashefipour & Falconer 2002;Carr & Rehmann 2007;Riahi-Madvar et al 2009;Ahmad 2013). These streams had 495 sample points that were split into a training set with 396 sample points and a testing set with 99 sample points.…”

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confidence: 99%

Estimation of the longitudinal dispersion coefficient via a fusion of optimized models

Gholami

Singh

2022

Journal of Hydroinformatics

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Determination of the longitudinal dispersion coefficient (LDC) is fundamental to the development of strategies for environmental management of river systems. This paper presents an integrated model for an estimation of the longitudinal dispersion coefficient by a fusion of optimized intelligent models (optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)) via committee machine (CM), with optimization done by the Bat-inspired algorithm (BA). The optimization eliminates the associated loss of accuracy of the intelligent models, which is a direct consequence of an improper adjustment of parameters (weights and biases in the neural network, membership's functions in the fuzzy inference system, and user-defined parameters in support vector regression). Data gathered from literature are employed to validate the proposed integrated model. A comparison between the optimized models and a committee machine, based on statistical parameters, shows that the committee machine model can attain high accuracy. Sensitivity analysis (SA) shows the contribution of each optimized model to the committee machine and ranks the contribution of the optimized models in ascending order as optimized neural network, optimized fuzzy inference system, and optimized support vector regression, each significantly correlated with the accuracy of longitudinal dispersion coefficient prediction.

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Prediction of longitudinal dispersion coefficient using laboratory and field data: relationship comparisons

Cited by 22 publications

References 19 publications

Experimental assessment and prediction of temporal scour depth around a spur dike

Experimental assessment and prediction of temporal scour depth around a spur dike

Scour around Spur Dike in Sand–Gravel Mixture Bed

Estimation of the longitudinal dispersion coefficient via a fusion of optimized models

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