Wash Load and Bed-Material Load Transport in the Yellow River

Yang, Chih Ted; Simões, Francisco J. M.

doi:10.1061/(asce)0733-9429(2005)131:5(413)

Cited by 30 publications

(16 citation statements)

References 2 publications

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“…Previous comparisons have shown that none of the formulas achieved an r values that was very close to unity for all measurements (Bravo-Espinosa et al 2003;Yang and Simoes, 2005). Fig.…”

Section: Evaluation Based On Frequency Of R Valuesmentioning

confidence: 52%

Bedload Equation Analysis Using Bed Load-Material Grain Size

Haddadchi¹,

Omid²,

Dehghani³

2013

Journal of Hydrology and Hydromechanics

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Twelve predictive bedload sediment transport equations are rated against 14 sets of gravel-bed river field data collected by handheld bedload sampler in Narmab River, northeastern Iran. To evaluate these formulas two types of grain size namely bedload and bed material were used. The results show that the equations of Engelund and Hansen, Van Rijn and Einstein perform well with bed material grain size, while Shocklitsch, Meyer-Peter and Mueller, and Frijlink yield the best results using the bedload grain size.

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“…Previous comparisons have shown that none of the formulas achieved an r values that was very close to unity for all measurements (Bravo-Espinosa et al 2003;Yang and Simoes, 2005). Fig.…”

Section: Evaluation Based On Frequency Of R Valuesmentioning

confidence: 52%

Bedload Equation Analysis Using Bed Load-Material Grain Size

Haddadchi¹,

Omid²,

Dehghani³

2013

Journal of Hydrology and Hydromechanics

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show abstract

“…The discrepancy ratio indicates the goodness-of-fit between the predicted and observed results. One of the ways to measure the goodnessof-fit is the use of standard deviation (Yang and Simôes, 2005) based on the average value of the logarithm ratio between computed and measured results using the following equation: Two standard training algorithms, the Levenberg-Marquardt (LM) optimization and the BFGS (Broyden, Fletcher, Goldfarb, and Shanno) quasi-Newton method, have been used to compare the GA-ANN results. Table 3 enlists the results.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Hydraulic Conductivity of Soil Bentonite Mixture Using Hybrid-ANN Approach

Mishra¹

2015

J ENV INFORM

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ABSTRACT. Due to its low hydraulic conductivity compacted soil-bentonite mixture is widely used as a barrier material at waste disposal site. The experimental determination of hydraulic conductivity of soil-bentonite mixture, which depends on the various physical and chemical and mineralogical factors, requires expensive and time consuming setup. Thus, a hybrid neural network model (combining genetic algorithm with neural network) is presented here as a complementary tool to model hydraulic conductivity of soil-bentonite mixture. The prediction capability of the model has been found to be satisfactory. The developed model yielded correlation coefficients of 0.98 and 0.97 for training and testing data sets, respectively. The proposed model was compared with conventional neural network models by using different statistical indicators such as Nash-Sutcliffe model efficiency and discrepancy ratio with standard deviation. It was found that the predictions obtained from developed model agreed well with experimental observations. Identification of important parameters and ranking their order of influence on hydraulic conductivity has been discussed by using input significance test.

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“…The discrepancy ratio indicates the goodness-of-fit between the predicted and observed results. One way to measure the goodness-of-fit is by use of the average discrepancy ratio and standard deviation (Yang and Simôes 2005) based on the average value of the logarithm ratio between the computed and measured:…”

Section: Input Layermentioning

confidence: 99%

Neural network prediction of bed material load transport

Kumar

2012

Hydrological Sciences Journal

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Editor D. KoutsoyiannisCitation Kumar, B., 2012. Neural network prediction of bed material load transport. Hydrological Sciences Journal, 57 (5), 956-966. Abstract Bed material load, which comprises bed load and suspended load, has been extensively studied in the past few decades and many equations have been developed, but they differ from each other in derivation and form. If a process can be related to various flow conditions on a general basis, a proper understanding of bed material load movement can be ascertained. As the process is extremely complex, obtaining a deterministic or analytical form of it is too difficult. Neural network modelling, which is particularly useful in modelling processes about which knowledge of the physics is limited, is presented here as a complimentary tool for modelling bed material load transport. The developed model demonstrated a superior performance compared to other traditional methods based on different statistical criteria, such as the coefficient of determination, Nash-Sutcliffe coefficient and discrepancy ratio. The significance of the different input parameters has been analysed in the present work to understand the influence of these parameters on the transport process. Prévision du charriage par réseau de neuronesRésumé Le charriage, qui comprend une charge de fond et une charge en suspension, a été abondamment étudié au cours des dernières décennies et de nombreuses équations ont été proposées, qui diffèrent les unes des autres par leur mode d'élaboration et par leur forme. Si, de façon générale, le processus peut être relié aux conditions de l'écoulement, on peut aussi chercher á comprendre correctement le mouvement des matériaux charriés. Comme le processus est extrêmement complexe, il est trop difficile d'en obtenir une forme déterministe ou analytique. La modélisation par réseaux de neurones, qui est particulièrement utile dans la modélisation des processus dont on connaît imparfaitement la physique, se présente ici comme un outil complémentaire pour le modélisation du charriage. Le modèle développé a démontré une performance supérieure á celles des méthodes traditionnelles basées sur différents critères statistiques, tels que le coefficient de détermination, le coefficient de Nash-Sutcliffe et le taux de divergence. Nous avons dans ce travail analysé la signification des différents paramètres d'entrée afin de comprendre leur influence sur le processus de transport.

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Wash Load and Bed-Material Load Transport in the Yellow River

Cited by 30 publications

References 2 publications

Bedload Equation Analysis Using Bed Load-Material Grain Size

Bedload Equation Analysis Using Bed Load-Material Grain Size

Prediction of Hydraulic Conductivity of Soil Bentonite Mixture Using Hybrid-ANN Approach

Neural network prediction of bed material load transport

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