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

Güven, Aytaç

doi:10.1016/j.advengsoft.2010.12.005

Cited by 24 publications

(10 citation statements)

References 24 publications

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“…Both the dimensional and non-dimensional parameters have been used in literature for the analysis of scouring phenomena using regression-based and artificial intelligence methods. It has been reported that the dimensionless parameters are more appropriate for modeling scour parameters [1,17]. In the present study, both the dimensional and non-dimensional parameters were used to simulate the scour-hole characteristics to assess the impacts of pre-processing of input data in model performance.…”

Section: Laboratory Experiments Of Scouring In Sluice Gatementioning

confidence: 99%

“…Guven and Gunal [16] used explicit neural networks formulations (ENNF) to predict local scour in the downstream of grade-control structures. A multi-output descriptive neural network (DNN) was developed by Reference [17] to estimate scour geometry in the downstream of hydraulic structures. The genetic programming (GP) method was applied to predict scouring depth in the downstream of ski-jump bucket spillway [18].…”

Section: Introductionmentioning

confidence: 99%

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Open Channel Sluice Gate Scouring Parameters Prediction: Different Scenarios of Dimensional and Non-Dimensional Input Parameters

Yousif

Sulaiman

Diop

et al. 2019

Water

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The determination of scour characteristics in the downstream of sluice gate is highly important for designing and protection of hydraulic structure. The applicability of modern data-intelligence technique known as extreme learning machine (ELM) to simulate scour characteristics has been examined in this study. Three major characteristics of scour hole in the downstream of a sluice gate, namely the length of scour hole (Ls), the maximum scour depth (Ds), and the position of maximum scour depth (Lsm), are modeled using different properties of the flow and bed material. The obtained results using ELM were compared with multivariate adaptive regression spline (MARS). The dimensional analysis technique was used to reduce the number of input variable to a smaller number of dimensionless groups and both the dimensional and non-dimensional variables were used to model the scour characteristics. The prediction performances of the developed models were examined using several statistical metrics. The results revealed that ELM can predict scour properties with much higher accuracy compared to MARS. The errors in prediction can be reduced in the range of 79%–81% using ELM models compared to MARS models. Better performance of the models was observed when dimensional variables were used as input. The result indicates that the use of ELM with non-dimensional data can provide high accuracy in modeling complex hydrological problems.

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Section: Laboratory Experiments Of Scouring In Sluice Gatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Open Channel Sluice Gate Scouring Parameters Prediction: Different Scenarios of Dimensional and Non-Dimensional Input Parameters

Yousif

Sulaiman

Diop

et al. 2019

Water

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“…For a more detailed analysis of the classification accuracy of MGGP, its sensitivity, specificity, positive predictivity, and accuracy are obtained using (4)(5)(6)(7). In general, the classification performance is presented by a confusion matrix as shown in Table 2.…”

Section: Application To Structural Engineering Problemsmentioning

confidence: 99%

“…Artificial neural networks (ANNs) are the most widely used pattern-recognition procedures. ANNs have been used for a wide range of materials and structural engineering problems [3][4][5][6][7]. Despite the acceptable performance of ANNs in most cases, they do not usually give a definite function to calculate the outcome using the input values.…”

Section: Introductionmentioning

confidence: 99%

A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

Gandomi

Alavi

2011

Neural Comput & Applic

267

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This paper presents a new approach for behavioral modeling of structural engineering systems using a promising variant of genetic programming (GP), namely multi-gene genetic programming (MGGP). MGGP effectively combines the model structure selection ability of the standard GP with the parameter estimation power of classical regression to capture the nonlinear interactions. The capabilities of MGGP are illustrated by applying it to the formulation of various complex structural engineering problems. The problems analyzed herein include estimation of: (1) compressive strength of high-performance concrete (2) ultimate pure bending of steel circular tubes, (3) surface roughness in end-milling, and (4) failure modes of beams subjected to patch loads. The derived straightforward equations are linear combinations of nonlinear transformations of the predictor variables. The validity of MGGP is confirmed by applying the derived models to the parts of the experimental results that are not included in the analyses. The MGGP-based equations can reliably be employed for pre-design purposes. The results of MSGP are found to be more accurate than those of solutions presented in the literature. MGGP does not require simplifying assumptions in developing the models.

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“…In the recent decade, in case of scouring at downstream of hydraulic structures, di erent Arti cial Intelligence (AI) models such as Arti cial Neural Networks (ANNs), Arti cial Neuro-Fuzzy Inference System (AN-FIS), Genetic Programming (GP), Gene-Expression Programming (GEP), and Group Method of Data Handling (GMDH) have been applied to predict the scour depth. From these applications, predictive methods based on iterative and evolutionary algorithms were established, and promisingly good validations were yielded for the measured dataset in comparison with empirical equations based regression [11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Gene-expression programming to predict the local scour depth at downstream of stilling basins

et al. 2016

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This research introduces an approach to predict maximum scour depth, scour hole length, and the length of the deepest point of the scour hole at equilibrium conditions at downstream of stilling basins by using the GEP. Five non-dimensional parameters in terms of physical properties of bed sediments, stilling basin length, tail water depth, and discharge of spillway were considered as input variables to evaluate the scour hole geometry. The GEP model was developed using experimental datasets collected from literature. Results of the GEP models were compared with those obtained using ANFIS and non-linear regression analysis. Performances indicated that the proposed GEP models to characterize scour hole geometry produced more accurate results than the other methods. In addition, results of sensitivity analysis to de ne the most e ective independent parameters on scour hole geometry were reported. Finally, proper application of the proposed model has been con rmed as the GEP-based best formulation, which is a useful soft computing tool for prediction of scour hole geometry at downstream of a stilling basin.

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

Cited by 24 publications

References 24 publications

Open Channel Sluice Gate Scouring Parameters Prediction: Different Scenarios of Dimensional and Non-Dimensional Input Parameters

Open Channel Sluice Gate Scouring Parameters Prediction: Different Scenarios of Dimensional and Non-Dimensional Input Parameters

A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

Gene-expression programming to predict the local scour depth at downstream of stilling basins

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