Artificial neural network approaches for prediction of backwater through arched bridge constrictions

Pinar, Engin; Paydas, Kamil; Seçkin, Galip; Akıllı, Hüseyin; Şahi̇n, Beşi̇r; Çobaner, Murat; Kocaman, Selahattin; Akar, Mustafa Atakan

doi:10.1016/j.advengsoft.2009.12.003

Cited by 26 publications

(15 citation statements)

References 36 publications

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“…In fact, almost all experimental studies on local scour aim to characterize the local scour, d m , and estimate it based on incoming flow parameters and scour geometry characteristics [29]. Due to the complex structure of scouring process together with difficulties raised by the various turbulent-flow conditions, most of the studies dealing with local scour have been done in experimental laboratory settings.…”

Section: Introductionmentioning

confidence: 99%

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A multi-output descriptive neural network for estimation of scour geometry downstream from hydraulic structures

Güven

2011

Advances in Engineering Software

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Section: Introductionmentioning

confidence: 99%

“…methods. Soft-computing techniques have been widely used and well-validated in estimation of water-resources variables, primarily using laboratory data [2,11,17,26,29]. Neural networks (NNs), a branch of soft-computing techniques, have been successively applied in estimation of hydraulic processes.…”

Section: Introductionmentioning

confidence: 99%

A multi-output descriptive neural network for estimation of scour geometry downstream from hydraulic structures

Güven

2011

Advances in Engineering Software

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“…In the field of backwater level prediction, ANNs were also used with different parameters. Many researchers have recently used ANNs to predict the backwater level and related parameters (Bilhan et al, 2010;Cobaner et al, 2008;Mamak et al, 2009;Pinar et al, 2010;Seckin et al, 2009;Unal et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Prediction of backwater level of bridge constriction using an artificial neural network

Atabay¹,

Abdalla²,

Erduran³

et al. 2013

Proceedings of the Institution of Civil Engineers - Water Manag

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Bridge constriction in channels usually increases the water level well above the normal depth and may result in overflow on the surrounding floodplain. In this paper, the experimental backwater level at which the maximum afflux value was observed due to bridge constriction was investigated. An artificial neural network (ANN) was used to predict the backwater level based on Manning's roughness coefficient of the main channel (nmc) and of the floodplain (nfp), bridge width (b) and flow discharge (Q). A multi-layer perceptron (MLP) ANN was used to predict the backwater level using these parameters. Multiple linear (MLR) regression and multiple non-linear regression (MNLR) were used as benchmarks for comparison of ANN results. It is concluded that an ANN can very accurately predict the backwater level. The developed ANN model was then used to conduct a parametric study to investigate the influence of nmc, nfp, b and Q on the backwater level due to a bridge constriction without piers. It is concluded that nmc and Q have a more profound effect on the backwater level than does nfp, while b has very little effect on the backwater level within this range of parameters. Other observations and conclusions are also drawn.

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“…River flow modeling is a very popular approach in science and education, as well as in small, medium-size, and large technical projects in the areas of water management [1,2], river regulation [3,4], sediment transport [5], flood protection [6], and many others [7,8]. There are several professionally prepared and widely used commercial and non-commercial models for river flow, e.g., HEC-RAS [9], MIKE 11 [10], Delft Sobek [11], BASEMENT by ETH [12], and SRH1D [13].…”

Section: Introductionmentioning

confidence: 99%

Application of Python Scripting Techniques for Control and Automation of HEC-RAS Simulations

Dysarz

2018

Water

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The purpose of the paper was to present selected techniques for the control of river flow and sediment transport computations with the programming language Python. The base software for modeling of river processes was the well-known and widely used HEC-RAS. The concepts were tested on two models created for a single reach of the Warta river located in the central part of Poland. The ideas described were illustrated with three examples. The first was a basic simulation of a steady flow run from the Python script. The second example presented automatic calibration of model roughness coefficients with Nelder-Mead simplex from the SciPy module. In the third example, the sediment transport was controlled by Python script. Sediment samples were accessed and changed in the sediment data file stored in XML format. The results of the sediment simulation were read from HDF5 files. The presented techniques showed good effectiveness of this approach. The paper compared the developed techniques with other, earlier approaches to control of HEC-RAS computations. Possible further developments were also discussed.

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Artificial neural network approaches for prediction of backwater through arched bridge constrictions

Cited by 26 publications

References 36 publications

A multi-output descriptive neural network for estimation of scour geometry downstream from hydraulic structures

A multi-output descriptive neural network for estimation of scour geometry downstream from hydraulic structures

Prediction of backwater level of bridge constriction using an artificial neural network

Application of Python Scripting Techniques for Control and Automation of HEC-RAS Simulations

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