GMDH to predict scour depth around a pier in cohesive soils

Najafzadeh, Mohammad; Barani, Gholam‐Abbas; Azamathulla, Hazi Mohammad

doi:10.1016/j.apor.2012.12.004

Cited by 96 publications

(30 citation statements)

References 34 publications

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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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Section: Methodsmentioning

confidence: 99%

GMDH algorithms applied to turbidity forecasting

Tsai

Yen

2016

Appl Water Sci

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“…The data related to scouring in cohesive soils was removed because the time required to reach the maximum scour depth was so longer compared to granular soils [33]. The data related to bridges with large accumulation of debris from past floods was also removed due to the high scouring caused by them.…”

Section: Utilized Datamentioning

confidence: 99%

Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

Hassanzadeh¹,

Jafari-Bavil-Olyaei²,

Taghi-Aalami³

et al. 2019

Period. Polytech. Civil Eng.

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An accurate estimation of bridge pier scour has been considered as one of the important parameters in designing of bridges. However, due to the numerous involved parameters and convolution of this phenomenon, many existing approaches cannot predict scour depth with an acceptable accuracy. Obtained results from the empirical relationships show that these relationships have low accuracy in determining the maximum scour depth and they need a high safety factor for many cases, which leads to uneconomic designs of bridges. To cover these disadvantages, three new models are provided to estimate the bridge pier scour using an adaptive networkbased fuzzy inference system. The parameters of the system are optimized by using the colliding bodies optimization, enhanced colliding bodies optimization and vibrating particles system methods. To evaluate the efficiency of the proposed methods, their results were compared with those of simple adaptive network-based fuzzy inference system and its improved versions by using the particle swarm optimization and genetic algorithm as well as the empirical equations. Comparison of results showed that the new vibrating particles system based algorithm could find better results than other two ones. In addition, comparison of the results obtained by the proposed methods with those of the empirical relations confirmed the high performance of the new methods.

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“…Nowadays, with advances in soft computing techniques in most areas of engineering, specifically in water engineering, researchers have attempted to apply them to increase the accuracy of modelling. In this regard, modelling the scouring phenomena (Najafzadeh and Azamathulla, ; Najafzadeh et al ., , , b; Najafzadeh and Tafarojnoruz, ), hydraulic and hydrologic indices (Noori et al ., , , , ) and water quality parameters can be stated (Azamathulla and Ghani, ; Azamathulla and Wu, ; Parsaie and Haghiabi, ). In this study, to predict the discharge coefficient of triangular labyrinth weirs, 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 are developed and their performance is compared.…”

Section: Introductionmentioning

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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GMDH to predict scour depth around a pier in cohesive soils

Cited by 96 publications

References 34 publications

GMDH algorithms applied to turbidity forecasting

GMDH algorithms applied to turbidity forecasting

Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

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

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