Neural network and neuro-fuzzy assessments for scour depth around bridge piers

Bateni, Sayed M.; Borghei, Seyed Mehdi; Jeng, Dong‐Sheng

doi:10.1016/j.engappai.2006.06.012

Cited by 182 publications

(71 citation statements)

References 25 publications

(40 reference statements)

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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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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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show abstract

“…Selection of an appropriate number of nodes in the hidden layer is a very important factor since a larger number of these nodes may result in over-fitting, while a smaller number of nodes may not capture the information adequately. The number of nodes in the hidden layer(s) can be determined by trial and error (Bateni et al 2007;Eberhart and Dobbins 1990). Fletcher and Goss (1993) suggested that the appropriate number of nodes in a hidden layer ranges from (2n 1/2 ?…”

Section: Mlp/bpmentioning

confidence: 99%

“…For example, Kuok et al (2009) have used particle swarm optimization feed-forward neural network for modeling runoff. Bateni et al (2007) have used ANNs and ANFIS models to estimate the equilibrium and time-dependent scour depth with numerous reliable database. ANNs have been used intensively in the development of a reservoir water quality simulation model (Soyupak et al 2003;Chaves and Kojiri 2007).…”

Section: Introductionmentioning

confidence: 99%

Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models

Emamgholizadeh

Kashi

Marofpoor

et al. 2013

Int. J. Environ. Sci. Technol.

131

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This paper describes the application of multilayer perceptron (MLP), radial basis network and adaptive neuro-fuzzy inference system (ANFIS) models for computing dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD) levels in the Karoon River (Iran). Nine input water quality variables including EC, PH, Ca, Mg, Na, Turbidity, PO 4 , NO 3 and NO 2 , which were measured in the river water, were employed for the models. The performance of these models was assessed by the coefficient of determination R 2 , root mean square error and mean absolute error. The results showed that the computed values of DO, BOD and COD using both the artificial neural network and ANFIS models were in close agreement with their respective measured values in the river water. MLP was also better than other models in predicting water quality variables. Finally, the sensitive analysis was done to determine the relative importance and contribution of the input variables. The results showed that the phosphate was the most effective parameters on DO, BOD and COD.

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“…On the other hand, these methods usu-ally result in conservative predictions [6]. Making physical models is expensive as well as time consuming.…”

Section: Empirical and Theoretical Prediction Methodsmentioning

confidence: 99%

Application of Artificial Neural Networks in Assessing the Equilibrium Depth of Local Scour Around Bridge Piers

Sarshari

Müllhaupt

2015

Volume 7: Ocean Engineering

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Scour can have the effect of subsidence of the piers in bridges, which can ultimately lead to the total collapse of these systems. Effective bridge design needs appropriate information on the equilibrium depth of local scour. The flow field around bridge piers is complex so that deriving a theoretical model for predicting the exact equilibrium depth of local scour seems to be near impossible. On the other hand, the assessment of empirical models highly depends on local conditions, which is usually too conservative.In the present study, artificial neural networks are used to estimate the equilibrium depth of the local scour around bridge piers. Assuming such equilibrium depth is a function of five variables, and using experimental data, a neural network model is trained to predict this equilibrium depth. Multilayer neural networks with backpropagation algorithm with different learning rules are investigated and implemented. Different methods of data normalization besides the effect of initial weightings and overtraining phenomenon are addressed. The results show well adoption of the neural network predictions against experimental data in comparison with the estimation of empirical models.

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Neural network and neuro-fuzzy assessments for scour depth around bridge piers

Cited by 182 publications

References 25 publications

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

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

Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models

Application of Artificial Neural Networks in Assessing the Equilibrium Depth of Local Scour Around Bridge Piers

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