Alternative neural networks to estimate the scour below spillways

Azamathulla, Hazi Mohammad; Deo, M. C.; Deolalikar, P. B.

doi:10.1016/j.advengsoft.2007.07.004

Cited by 104 publications

(26 citation statements)

References 21 publications

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“…A review of literature reveals that majority of past works on scour calculations utilized the hydraulic model studies, which were more assist in investigating the scour mechanism than in obtaining more accuracy in the depth prediction. However, they suffer from the problems arising out of the scale effects, inability to correctly model certain field conditions like bed morphology and loss of flow energy in aeration as well as failure to consider a variety of causative factors simultaneously (Azmathullah et al 2008a). The coefficient of correlation (CC) and the root mean square error (RMSE) values were used for the evaluation of the performance of SVM and M5P models.…”

Section: Datasets Used and Methodologymentioning

confidence: 99%

“…In the recent past, conventional statistical analysis is now being replaced in many cases by the alternative approach of neural networks and it has been demonstrated that artificial neural network (ANN) predictions were generally more satisfactory than those given by traditional regression equations because of low errors and high correlation coefficients (Azmathullah et al 2005(Azmathullah et al , 2006(Azmathullah et al , 2008aAltunkaynak 2007;Goyal and Ojha 2010a, b;Ojha et al 2010). Successful applications of machine learning approaches in water resources management by Altunkaynak (2007), Hong and Pai (2007), Pal and Goel (2007), Azmathullah et al (2008bAzmathullah et al ( , 2009, Lee et al (2008), Sohail et al (2008), Nourani et al (2009), and have inspired the exploration of their applicability to model various complex water resources problems.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree

Goyal

Ojha

2011

Water Resour Manage

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Estimation of scour downstream of a ski-jump bucket has been a topic of research among hydraulic engineers. For estimation of scour downstream of ski jump bucket, several empirical models are in use. In recent years, there has been emphasis to develop models which are capable of producing scour with high accuracy. Use of Artificial Neural Network (ANN) approach to model depth, width and length of scour hole indicates that performance of ANN models is far better than existing empirical models. At present, use of Support Vector Machines (SVMs) and M5 Pruned Model Tree are being considered in different disciplines to further improve upon the performance of ANN models as a potential alternate. With this in view, the present study deals with the development of regression models for computing various parameters of scour hole using SVMs and M5 Model Tree. A comparative evaluation of the performance of ANN versus SVMs and M5 Model Tree clearly shows that SVMs and M5 Model Tree can prove more useful than ANN models in estimation of scour downstream of a ski jump bucket. Further, M5 model tree offers explicit expressions for use by design engineers. Notation dsMaximum depth of scour below tail water level (m); dwTail water depth (m); d50Mean sediment size (m); H1 Head between upper reservoir water level and tail water level (m);

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Section: Datasets Used and Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree

Goyal

Ojha

2011

Water Resour Manage

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“…In the eld of mathematical modeling, using both of CFD and soft computing techniques was reported by Xiao et al [11].Nowadays, by advancing the soft computing techniques in the most areas related to hydraulic engineering, investigators have tried to use these techniques for predicting the scouring phenomena [12][13][14][15][16][17][18][19], speci cally scour depth at downstream of ip bucket. In this regard, using the Arti cial Neural Networks (ANNs), Genetic Programming (GP), Support Vector machine and M5 Model Tree, Group Method of Data Handling (GMDH), and Adaptive Neuro Fuzzy Inference System (ANFIS) can be mentioned [20][21][22][23][24][25][26][27][28][29][30][31][32]. Based on the reports, the precision of all the soft computing techniques was much more than the empirical formulas.…”

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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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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“…They also used some traditional methods. Their study showed that the traditional equation-based methods of predicting design scour downstream of a ski-jump bucked could better be replaced by one of the soft computing schemes [8]. Salmasi and Özger [9], investigated the applicability and accuracy of the neuro-fuzzy approach in estimating the proper values of energy dissipation of skimming flow regime over stepped spillways.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have studied for numerical and experimental studies on spillways [3][4][5][6][7]. And ANN [8], studied for scour depth prediction for ski-jump type of spillways. They also used some traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Analytical and artificial neural network models to estimate the discharge coefficient for ogee spillway

Yarar

2017

E3S Web Conf.

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Abstract. In this study, analytical and Artificial Neural Network (ANN) model were used for determine the discharge coefficient of Ogee Spillways. For this aim, discharge coefficients of 11 different heads were calculated by using a test flume having 7.5 cm width, 15 cm depth and 5 m length, in the laboratory. Discharge coefficients were also computed by the formula for the same heads measured in the laboratory to investigate the accuracy of experimental setup. An ANN model was set by using the experimental results in order to estimate the discharge coefficient. Then, the performance of the ANN model was investigated. As the result, the coefficient of determination between ANN model and experimental setup is found R 2 = 0.98. ANN model is show a good consistency with experimental results.

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Alternative neural networks to estimate the scour below spillways

Cited by 104 publications

References 21 publications

Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree

Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree

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

Analytical and artificial neural network models to estimate the discharge coefficient for ogee spillway

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