Optimum Design of Lined Channel Sections

Niazkar, Majid; Afzali, Seied Hosein

doi:10.1007/s11269-015-0919-9

Cited by 30 publications

(18 citation statements)

References 15 publications

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“…Because the water depth at the upstream river section is commonly the lowest in a river segment, the discharge needed at this section is calculated as the required e-flow. Manning's Equation is a function of channel velocity, flow area, and channel slope [22]:…”

Section: E-flow Determination Under Medium Hydrological Connectivitymentioning

confidence: 99%

The Influence of Channel Morphological Changes on Environmental Flow Requirements in Urban Rivers

Zhang¹,

Yuan²,

Yin

et al. 2019

Water

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Previous research on environmental flows (e-flows) of urban rivers usually assumes that the channel morphology is fixed. However, due to the trapping of sediments by weirs, the channel morphology will undergo significant changes. In this research, the influence of channel morphological changes on e-flow requirements is explored in urban rivers. The hydrological connectivity is considered as a primary factor in e-flows, and three hydrological connectivity scenarios (i.e., high, medium, and low) are explored. The Shiwuli River is adopted as the case study. The results show that e-flows are significantly influenced by changes in river morphology. With an increase in siltation depth, the e-flow requirements will decrease. The sensitivity of e-flows to siltation varies among different river segments, especially in those with low weir heights. In addition, the change ratios of e-flows are different under different hydrological connectivity scenarios. Although siltation is beneficial to the satisfaction degree of e-flow supply, it also leads to a decrease in the flood control ability of rivers. The balance between e-flow and flood reduction is also discussed, and river segments are identified that should be the priority when adopting dredging measures.

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Section: E-flow Determination Under Medium Hydrological Connectivitymentioning

confidence: 99%

The Influence of Channel Morphological Changes on Environmental Flow Requirements in Urban Rivers

Zhang¹,

Yuan²,

Yin

et al. 2019

Water

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“…Length. Steady GVF is one of the most common time-independent flow conditions occurred in open channels, while length of GVF profiles is necessary to channel design, design of hydraulic structures, and budget estimation of open-channel water conveyance projects [1,22]. In the current literature, Swamee [1] presented empirical relations between the control section and the section with 0.99y n or 1.01y n for triangular, wide, and narrow rectangular.…”

Section: Problem Statement Of Gvf Profilementioning

confidence: 99%

Assessment of Artificial Intelligence Models for Estimating Lengths of Gradually Varied Flow Profiles

2021

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The study of water surface profiles is beneficial to various applications in water resources management. In this study, two artificial intelligence (AI) models named the artificial neural network (ANN) and genetic programming (GP) were employed to estimate the length of six steady GVF profiles for the first time. The AI models were trained using a database consisting of 5154 dimensionless cases. A comparison was carried out to assess the performances of the AI techniques for estimating lengths of 330 GVF profiles in both mild and steep slopes in trapezoidal channels. The corresponding GVF lengths were also calculated by 1-step, 3-step, and 5-step direct step methods for comparison purposes. Based on six metrics used for the comparative analysis, GP and the ANN improve five out of six metrics computed by the 1-step direct step method for both mild and steep slopes. Moreover, GP enhanced GVF lengths estimated by the 3-step direct step method based on three out of six accuracy indices when the channel slope is higher and lower than the critical slope. Additionally, the performances of the AI techniques were also investigated depending on comparing the water depth of each case and the corresponding normal and critical grade lines. Furthermore, the results show that the more the number of subreaches considered in the direct method, the better the results will be achieved with the compensation of much more computational efforts. The achieved improvements can be used in further studies to improve modeling water surface profiles in channel networks and hydraulic structure designs.

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“…The detail of this algorithm is comprehensively presented in the literature [40][41] for interested readers. In this hybrid method, the MHBMO algorithm precedes the optimization process for several numbers of iterations and results of the last iteration will be used as initial guesses for the next step.…”

Section: The Hybrid Mhbmo-grg Algorithmmentioning

confidence: 99%

Bridge backwater estimation: A Comparison between artificial intelligence models and explicit equations

2020

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Estimation of bridge backwater has been one of practical challenges in hydraulic engineering for decades. In this study, Genetic Programming (GP) has been applied for estimating bridge backwater for the first time based on the conducted literature review. Furthermore, two new explicit equations are developed for predicting bridge afflux using Genetic Algorithm (GA) and hybrid MHBMO-GRG algorithm. The performances of these models are compared with Artificial Neural Network (ANN) and several explicit equations available in the literature considering both laboratory and field data. Based on five considered performance evaluation criteria, the two new explicit equations outperform the ones available in the literature. Furthermore, GP and ANN achieve the 2 best results in favor of four out of five considered criteria for train and test data, respectively. To be more specific, ANN improves the MSE and R 2 values of the explicit equation developed using GA by 44% and 12% for the test data while GP enhances the corresponding values by 62% and 9% for the train data. Finally, the results demonstrate that not only artificial intelligence models considerably improve bridge afflux estimation than the explicit equations but also the suggested equations significantly improve the accuracy of the available explicit ones.

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Optimum Design of Lined Channel Sections

Cited by 30 publications

References 15 publications

The Influence of Channel Morphological Changes on Environmental Flow Requirements in Urban Rivers

The Influence of Channel Morphological Changes on Environmental Flow Requirements in Urban Rivers

Assessment of Artificial Intelligence Models for Estimating Lengths of Gradually Varied Flow Profiles

Bridge backwater estimation: A Comparison between artificial intelligence models and explicit equations

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