Evaluation of GMDH networks for prediction of local scour depth at bridge abutments in coarse sediments with thinly armored beds

Najafzadeh, Mohammad; Barani, Gholam‐Abbas; Hessami-Kermani, Masoud-Reza

doi:10.1016/j.oceaneng.2015.05.016

Cited by 53 publications

(18 citation statements)

References 38 publications

(54 reference statements)

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“…The idea of crossbreeding and selection is similar to GA. As other types of neural networks, GMDH may involve one or more hidden layer(s). GMDH is a flexible algorithm that can be hybridized along with other meta‐heuristic algorithms such as GA, PSO, genetic programming, back propagations and Levenberg–Marquardt (Najafzadeh et al ., ; Najafzadeh and Barani, ; Najafzadeh and Sattar, ). The GMDH method has been used for modelling hydraulic and hydrological phenomena.…”

Section: Methodsmentioning

confidence: 99%

Improving Modelling of Discharge Coefficient of Triangular Labyrinth Lateral Weirs Using SVM, GMDH and MARS Techniques

Parsaie

Haghiabi

2017

Irrigation and Drainage

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In this study the discharge coefficient (Cd) of labyrinth lateral weirs was modelled and predicted using artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM), group method of data handling (GMDH), and multivariate adaptive regression splines (MARS) techniques. To this end, a related data set was collected from the literature. Results indicated that the proposed ANN model includes two hidden layers with eight and five neurons in the first and second hidden layers, respectively. Testing transfer functions demonstrated that the radial basis function (RBF) has the best outcome. Evaluating the performance of the ANN model shows that this model with a coefficient of determination (R2 = 0.96) and root mean square error (RMSE =0.07) in the testing stage has suitable functionality. Reviewing the ANFIS model showed that this model with one hidden layer containing seven membership functions within the Gaussian function could achieve R2 = 0.94 and RMSE =0.136 for predicting the discharge coefficient. During preparation of SVM, it was found that this model with RBF as the best kernel function obtained R2 = 0.96 and RMSE =0.07. The structure of the MARS and GMDH models pointed out that the Froude number, grandiose index and ratio of upstream flow depth to the height of weir are the most effective parameters for predicting the discharge coefficient. Minimum R2 and RMSE achieved were equal to 0.93 and 0.08, respectively, in the testing stage for the MARS and GMDH models. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Methodsmentioning

confidence: 99%

Improving Modelling of Discharge Coefficient of Triangular Labyrinth Lateral Weirs Using SVM, GMDH and MARS Techniques

Parsaie

Haghiabi

2017

Irrigation and Drainage

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“…Specific functional dependence between the input and output variables is unnecessary, as the dependence has been incorporated into the modeling structure. The GMDH algorithm has been applied in various fields, e.g., weather modeling, pattern recognition, physiological experiments, cybernetics, medical science, education, ecology, safety science, economics, and hydraulic field engineering systems (Lebow et al 1984;Ivakhnenko et al 1994;Kondo et al 1999;Chang and Hwang 1999;Sarycheva 2003;Pavel and Miroslav 2003;Hwang et al 2009;Tsai et al 2009;Najafzadeh et al 2013Najafzadeh et al , 2014Najafzadeh et al , 2015Najafzadeh 2015). Nevertheless, few studies have explored turbidity modeling.…”

Section: Methodsmentioning

confidence: 99%

GMDH algorithms applied to turbidity forecasting

Tsai

Yen

2016

Appl Water Sci

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By applying the group method of data handling algorithm to self-organization networks, we design a turbidity prediction model based on simple input/output observations of daily hydrological data (rainfall, discharge, and turbidity). The data are from a field test site at the Chiahsien Weir and its upper stream in Taiwan, and were recorded from May 2000 to December 2008. The model has a regressive mode that can assess the estimated error, i.e., whether a threshold has been exceeded, and can be adjusted by updating the field input data. Consequently, the model can achieve accurate estimations over long-term periods. Test results demonstrate that the 2006 turbidity prediction model was selected as the best predictive model (RMSE = 5.787 and CC = 0.975) because of its ability to predict turbidity within the acceptable error range and 90 % required confidence interval (50NTU). 70(3,1,1) is the optimum modeling data length and variable combinations.

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“…Neuro-fuzzy GMDH networks using evolutionary algorithms were proposed in Najafzadeh & Bonakdari, 2016), and the models were found to produce very precise predictions. GMDH networks developed using gravitational search algorithms (GSA), as well as particle swarm optimization (PSO) and back propagation (BP) algorithms have been utilized to predict scour at abutments in armored beds, and they have produced accurate prediction results (Najafzadeh et al, 2015). A GMDH gene expression programming (GMDH-GEP) model performed well in predicting free span expansion rates below pipelines under waves .…”

Section: Introductionmentioning

confidence: 99%

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Nayak

Misra²

2019

Financ Innov

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Accurate prediction of stock market behavior is a challenging issue for financial forecasting. Artificial neural networks, such as multilayer perceptron have been established as better approximation and classification models for this domain. This study proposes a chemical reaction optimization (CRO) based neuro-fuzzy network model for prediction of stock indices. The input vectors to the model are fuzzified by applying a Gaussian membership function, and each input is associated with a degree of membership to different classes. A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model. CRO was chosen because it requires fewer control parameters and has a faster convergence rate. Five statistical parameters are used to evaluate the performance of the model, and the model is validated by forecasting the daily closing indices for five major stock markets. The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior. We conducted the Deibold-Mariano test to check the statistical significance of the proposed model, and it was found to be significant. This model can be used as a promising tool for financial forecasting.

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Evaluation of GMDH networks for prediction of local scour depth at bridge abutments in coarse sediments with thinly armored beds

Cited by 53 publications

References 38 publications

Improving Modelling of Discharge Coefficient of Triangular Labyrinth Lateral Weirs Using SVM, GMDH and MARS Techniques

Improving Modelling of Discharge Coefficient of Triangular Labyrinth Lateral Weirs Using SVM, GMDH and MARS Techniques

GMDH algorithms applied to turbidity forecasting

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

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