Group Method of Data Handling to Predict Scour at Downstream of a Ski-Jump Bucket Spillway

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

doi:10.1007/s12145-013-0140-4

Cited by 44 publications

(13 citation statements)

References 36 publications

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“…Various studies have addressed scour prediction using feedforward neural networks (FFNN) [26]; gene expression programming (GEP) [27][28][29]; adaptive neuro-fuzzy inference systems (ANFIS) [30]; support vector machines (SVM) [31]; group method of data handling (GMDH) [32][33][34][35], and model trees [36]. Bateni and Jeng [37] applied an ANFIS-based method to estimate the scour at pile groups.…”

Section: Introductionmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

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Abutment scour is a complex three-dimensional phenomenon, which is one of the leading causes of marine structure damage. Structural integrity is potentially attainable through the precise estimation of local scour depth. Due to the high complexity of scouring hydrodynamics, existing regression-based relations cannot make accurate predictions. Therefore, this study presented a novel expansion of extreme learning machines (ELM) to predict abutment scour depth (ds) in clear water conditions. The model was built using the relative flow depth (h/L), excess abutment Froude number (Fe), abutment shape factor (Ks), and relative sediment size (d50/L). A wide range of experimental samples was collected from the literature, and data was utilized to develop the ELM model. The ELM model reliability was evaluated based on the estimation results and several statistical indices. According to the results, the sigmoid activation function (correlation coefficient, R = 0.97; root mean square error, RMSE = 0.162; mean absolute percentage error, MAPE = 7.69; and scatter index, SI = 0.088) performed the best compared with the hard limit, triangular bias, radial basis, and sine activation functions. Eleven input combinations were considered to investigate the impact of each dimensionless variable on the abutment scour depth. It was found that ds/L = f (Fe, h/L, d50/L, Ks) was the best ELM model, indicating that the dimensional analysis of the original data properly reflected the underlying physics of the problem. Also, the absence of one variable from this input combination resulted in a significant accuracy reduction. The results also demonstrated that the proposed ELM model significantly outperformed the regression-based equations derived from the literature. The ELM model presented a fundamental equation for abutment scours depth prediction. Based on the simulation results, it appeared the ELM model could be used effectively in practical engineering applications of predicting abutment scour depth. The estimated uncertainty of the developed ELM model was calculated and compared with the conventional and artificial intelligence-based models. The lowest uncertainty with a value of ±0.026 was found in the proposed model in comparison with ±0.50 as the best uncertainty of the other models.

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Section: Introductionmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

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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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“…The MARS model was developed by Friedman [33] and has been successfully applied up to now for predicting the river discharge forecasting, rainfall-runo modeling, etc. [34][35][36].…”

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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Group Method of Data Handling to Predict Scour at Downstream of a Ski-Jump Bucket Spillway

Cited by 44 publications

References 36 publications

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

GMDH algorithms applied to turbidity forecasting

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

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