Effect of Nonuniformity of Flow on Hydraulic Geometry Relations

Afzalimehr, Hossein; Singh, Vijay P.; Abdolhosseini, Mohammad

doi:10.1061/(asce)he.1943-5584.0000095

Cited by 16 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hey and Thorne () used empirical models and stated that channel width is most affected by changes in the power of Q (Neill, ). Afzalimehr et al () assessed the flow non‐uniformity effect on the geometry parameters of stable channels. They found that Q , d 50 and τ * are the main factors in controlling channel geometry parameters ( w , h and s ).…”

Section: Introductionmentioning

confidence: 99%

Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed

Gholami

Bonakdari

Ebtehaj

et al. 2017

Earth Surf Processes Landf

View full text Add to dashboard Cite

Predicting the geometry of channels and alluvial rivers is of primary importance in river engineering science. Appropriately designing channels and predicting stable river cross‐sections can decrease costs and prevent the destruction of installations and agricultural land by rivers. Consequently, researchers have applied different empirical and regression methods to achieve relations for predicting stable channel and river geometry. In this study, Group Method of Data Handling ]GMDH) models are used to predict three geometric variables of stable channels, namely width (w), depth (h) and slope (s). The effect of different input parameters, such discharge (Q), median grain size (d50) and the Shields parameter (τ*) on the GMDH models is assessed with regard to predicting stable channel geometry. The results indicate that the GMDH model with mean absolute percentage error (MAPE) of 5.53%, 4.05% and 4.89% for channel width, depth and slope prediction respectively, exhibits good accuracy. Moreover, a comparison of the GMDH models with previous theoretical equations (based on regression analysis) indicates the superiority of GMDH model performance, with error reductions of one‐fifth, one‐eighth and one‐sixth compared with the regression equations for channel width, depth and slope prediction, respectively. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Section: Introductionmentioning

confidence: 99%

Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed

Gholami

Bonakdari

Ebtehaj

et al. 2017

Earth Surf Processes Landf

View full text Add to dashboard Cite

show abstract

“…When applying one of the methods for predicting discharge, the range of cross-sectional data should be taken into account. In other words, these methods may not be applied to cases where the cross-sectional and discharge data are too small (A < 1 m 2 , Q < 1 m 3 /s, as it is the case in Afzalimehr et al 2009) or too extreme. In such cases, it would be necessary to recalibrate and re-validate the methods.…”

Section: Discussionmentioning

confidence: 99%

Predicting Mean and Bankfull Discharge from Channel Cross-Sectional Area by Expert and Regression Methods

Tayfur

Singh

2010

Water Resour Manage

Self Cite

View full text Add to dashboard Cite

This study employed four methods-non-linear regression, fuzzy logic (FL), artificial neural networks (ANNs), and genetic algorithm (GA)-based nonlinear equation-for predicting mean discharge and bank-full discharge from crosssectional area. The data compiled from the literature were separated into two groups-training (calibration) and testing (verification). Using training data sets, the methods were calibrated to obtain optimal values of the coefficients of the non-linear regression method; optimal number of fuzzy subsets, their base widths and fuzzy rules for the fuzzy method; and the optimal number of neurons in the hidden layer, the learning rate and momentum factor values for the ANN model. The GA-based method employed 100 chromosomes in the initial gene pool, 80% cross over rate and 4% mutation rate in determining the optimal values of the coefficients of the constructed nonlinear equation. The calibrated methods were then applied to the test data sets. The test results showed that the non-linear regression, ANN and GA-based methods were comparable in predicting the mean discharge while the fuzzy method produced high errors and low accuracy. The GA-based method had the highest accuracy of 75%. In terms of predicting bankfull discharge, all methods produced satisfactory results, although the fuzzy method had the lowest accuracy of 33%. The results of sensitivity analysis, which is limited to the GA-based and nonlinear regression methods, showed that the GA-based method calibrated with low bankfull discharge values can be successfully applied to predict high bankfull discharge values. This has important implications for predicting bankfull rates at ungauged sites. On the other hand, the sensitivity analysis results also showed that both the non-linear regression and GA-based methods have poor extrapolation capability for predicting mean discharge data.

show abstract

“…However, contrasting results on the most influential factors on depth and width have been found. For example, numerous studies have found discharge to be the most important factor, followed by Shields stress and d50 (Afzalimehr et al 2009(Afzalimehr et al , 2010Bray 1982;Hey and Thorne 1986a). Abdelhaleem et al (2016) showed that as well as flow discharge other controlling factorssuch as flow velocity must be incorporated to increase prediction accuracy.…”

Section: Effect Of Input Variables On Model Prediction Performance 529mentioning

confidence: 99%

Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms

Khosravi

Khozani

Cooper

2021

Environmental Modelling & Software

View full text Add to dashboard Cite

Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility.However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of hydraulic geometry is lacking. This study provides the first quantification of this potential. Using at-a-station field data, predictions of flow depth, water-surface width and longitudinal water surface slope are made using three standalone data mining techniques -, Instance-based Learning (IBK), KStar, Locally Weighted Learning (LWL) -along with four types of novel hybrid algorithms in which the standalone models are trained with Vote, Attribute Selected Classifier (ASC), Regression by Discretization (RBD), and Cross-validation Parameter Selection (CVPS) algorithms (Vote-IBK, Vote-

show abstract

Effect of Nonuniformity of Flow on Hydraulic Geometry Relations

Cited by 16 publications

References 8 publications

Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed

Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed

Predicting Mean and Bankfull Discharge from Channel Cross-Sectional Area by Expert and Regression Methods

Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms

Contact Info

Product

Resources

About