Neural Networks for Estimation of Scour Downstream of a Ski-Jump Bucket

Azmathullah, H. Md.; Deo, M. C.; Deolalikar, P. B.

doi:10.1061/(asce)0733-9429(2005)131:10(898)

Cited by 174 publications

(77 citation statements)

References 9 publications

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“…Schoklitsch (1932) with R 2 = 0:45 and RMSE=0.08 is accurate among the empirical formulas, as shown in this gure, and the poorest performance is related to Azar (1998); as seen, all the empirical formulas have no acceptable performances for practical purposes. The results of assessing the performance of empirical formula support those of the study conducted by Azmathullah et al [9] who stated that using the empirical formula for calculating the depth of scour has obvious errors compared to the measured data.…”

Section: Resultssupporting

confidence: 78%

“…The USBR conducted an extensive study on the scouring depth at downstream of ip bucket and proposed empirical formula for calculating the depth of scour [7,8]. Azmathullah et al [9] assessed the accuracy of USBR formula for calculating the depth of scour at downstream of spillway of Rana Pratap Sagar Dam which was constructed across the Chamba River, and found that calculating the scour depth using the USBR formula is equal to 30 m, whereas the measured data is equal to 24.7 m. In other words, using the USBR formula causes about 30 percent error for predicting the scour depth. After USBR, several empirical studies have been conducted on the scour depth of calculation at the downstream of ip bucket structure.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of scour downstream of a ski-jump bucket using the multivariate adaptive regression splines

Haghiabi

2017

Scientia Iranica

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Abstract. In this paper, modeling the scour downstream of a ip bucket of spillways was considered using empirical formulas, soft computing techniques such as multilayer perceptron (MLP) neural network, and Multivariate Adaptive Regression Splines (MARS). For this purpose, 95 data sets were collected with regard to the most a ective parameters on the scouring phenomena at downstream of spillways. During the MLP model development, it was found that the two transfer functions, such as log-sigmoid and radial basis, had very suitable performances for predicting the desired scouring phenomena. The results of MARS model showed that this model with coe cient of determination 0.99 and 0.91 during the development and testing stages, respectively, had suitable performance for modeling the scouring depth at downstream of ip bucket structure. The results of gamma test and MARS model indicated that q=(gd 3 w ), R=d w , and H=d w were the most a ective parameters on the scouring phenomena.

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Section: Resultssupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Estimation of scour downstream of a ski-jump bucket using the multivariate adaptive regression splines

Haghiabi

2017

Scientia Iranica

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“…RBF networks have not been used extensively in environmental studies except a few researchers such as Azmathullah et al (2005), Bateni et al (2007) and Tahershamsi et al (2012). A typical RBF network has a feedforward structure consists of an input layer, a hidden layer with a radial basis activation function, and a linear output layer.…”

Section: Overview Of Ffbp Grnn and Rbf Algorithmsmentioning

confidence: 99%

Successive-station monthly streamflow prediction using different artificial neural network algorithms

Mehr

Kahya

ŞAHİN

et al. 2014

Int. J. Environ. Sci. Technol.

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In this study, applicability of successive-station prediction models, as a practical alternative to streamflow prediction in poor rain gauge catchments, has been investigated using monthly streamflow records of two successive stations on Ç oruh River, Turkey. For this goal, at the first stage, based on eight different successive-station prediction scenarios, feed-forward back-propagation (FFBP) neural network algorithm has been applied as a brute search tool to find out the best scenario for the river. Then, two other artificial neural network (ANN) techniques, namely generalized regression neural network (GRNN) and radial basis function (RBF) algorithms, were used to generate two new ANN models for the selected scenario. Ultimately, a comparative performance study between the different algorithms has been performed using Nash-Sutcliffe efficiency, squared correlation coefficient, and root-meansquare error measures. The results indicated a promising role of successive-station methodology in monthly streamflow prediction. Performance analysis showed that only 1-month-lagged record of both stations was satisfactory to achieve accurate models with high-efficiency value. It is also found that the RBF network resulted in higher performance than FFBP and GRNN in our study domain.

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“…In order to model scouring, a variety of soft computing schemes have been used in recent years, such as an artificial neural network (ANN) to predict scour below a ski-jump bucket spillway (Azmathullah, Deo, & Deolalikar, 2005) and culvert outlets (Azamathulla & Haque, 2012), classification and regression trees to estimate wave-induced scour around a circular pile (Ayoubloo, Etemad-Shahidi, & Mahjoobi, 2010), an M5 model tree to estimate scour downstream of a ski-jump bucket spillway (Goyal & Ojha, 2011), an adaptive network based on fuzzy systems to determine scour around an arch-shaped bed sill (Keshavarzi, Gazni, & Homayoon, 2012), linear genetic programming to predict scour around a circular pile (Guven, Azamathulla, & Zakaria, 2009), and the group method of data handling to predict scour downstream of a ski-jump bucket spillway (Najafzadeh, Barani, & Hessami Kermani, 2014).…”

Section: Introductionmentioning

confidence: 99%

Estimation of scour depth below free overfall spillways using multivariate adaptive regression splines and artificial neural networks

Samadi

Jabbari

Azamathulla

et al. 2015

Engineering Applications of Computational Fluid Mechanics

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Erosion and scouring caused by the outflow jets of hydraulic structures is one of the most important topics in hydraulic engineering. In free overfall spillways, a water jet impacts the erodible downstream bed almost vertically and creates a scour hole. The scour hole can affect the safety and stability of the dam. In the present paper, the multivariate adaptive regression splines (MARS) approach has been adopted as a new soft computing tool for estimating the equilibrium scour depth below free overfall spillways. Using experimental data and dimensionless parameters, the MARS model has been developed to predict scour depth. Results obtained from the MARS approach were compared with those from the artificial neural network (ANN) and decision-tree algorithm. Statistical indicators demonstrated that the MARS approach had a good performance and presented competitive results, slightly better than ANN, for the prediction of this phenomenon.

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Neural Networks for Estimation of Scour Downstream of a Ski-Jump Bucket

Cited by 174 publications

References 9 publications

Estimation of scour downstream of a ski-jump bucket using the multivariate adaptive regression splines

Estimation of scour downstream of a ski-jump bucket using the multivariate adaptive regression splines

Successive-station monthly streamflow prediction using different artificial neural network algorithms

Estimation of scour depth below free overfall spillways using multivariate adaptive regression splines and artificial neural networks

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