Prediction of pile group scour in waves using support vector machines and ANN

Ghazanfari-Hashemi, Samaneh; Etemad‐Shahidi, Amir; Kazeminezhad, Mohammad Hossein; Mansoori, Amir Reza

doi:10.2166/hydro.2010.107

Cited by 40 publications

(10 citation statements)

References 27 publications

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“…These methods have been recently used in different fields of engineering problems representing promising performance compared to the other soft computing methods (e.g. Ghazanfari-Hashemi et al 2011;Grbićet al 2013;Sun et al 2014;Roushangar & Koosheh 2015;Roushangar et al 2016;Najafzadeh & Oliveto 2020). To the best of authors' knowledge, SVR and GPR methods have not been implemented for the prediction of the mean wave overtopping rate so far.…”

Section: Introductionmentioning

confidence: 99%

Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods

Hosseinzadeh

Etemad‐Shahidi

Koosheh

2021

Journal of Hydroinformatics

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The accurate prediction of the mean wave overtopping rate at breakwaters is vital to have a safe design. Hence, providing a robust tool as a preliminary estimator can be useful for practitioners. Recently, soft computing tools such as artificial neural network (ANN) have been developed as alternatives to traditional overtopping formulae. The goal of this paper is to assess the capabilities of two kernel-based methods namely Gaussian process regression (GPR) and support vector regression for the prediction of mean wave overtopping rate at sloped breakwaters. An extensive dataset taken from EurOtop (2018) database, including rubble mound structures with permeable core, straight slopes, without berm, and crown wall, was employed to develop the models. Different combinations of the important dimensionless parameters representing structural features and wave conditions were tested based on the sensitivity analysis for developing the models. The obtained results were compared with those of the ANN model and the existing empirical formulae. The modified Taylor diagram was used to compare the models graphically. The results showed the superiority of kernel-based models, especially the GPR model over the ANN model and empirical formulae. In addition, the optimal input combination was introduced based on accuracy and the number of input parameters criteria. Finally, the physical consistencies of developed models were investigated the results, of which demonstrated the reliability of kernel-based models in terms of delivering physics of overtopping phenomenon.

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Section: Introductionmentioning

confidence: 99%

Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods

Hosseinzadeh

Etemad‐Shahidi

Koosheh

2021

Journal of Hydroinformatics

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“…Raikar et al (2016) applied ANN and genetic algorithms (GA) for prediction of scour depth within channel contractions. Many researchers also verified the efficacy of techniques like adaptive neuro-fuzzy inference system (ANFIS) and support vector machines (SVM) for predicting bridge scour (Bateni et al 2007;Muzzammil 2010;Ghazanfari-Hashemi et al 2011;Pal et al 2011;Hong et al 2012;Akib et al 2014;Khan et al 2014;Najafzadeh et al 2016;Chou & Pham 2017). More recently, several studies have reported the use of hybrid techniques for predicting bridge scour (Chou & Pham 2014;Jannaty et al 2015;Dang et al 2019).…”

Section: Introductionmentioning

confidence: 95%

Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers

Sreedhara

Patil²,

Pushparaj

et al. 2021

Journal of Hydroinformatics

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Scour around bridge piers is a complex phenomenon and it is essential to assess or predict the scour hazard around bridge piers in tandem with completely understanding its mechanism. To date, there is no exact method for the estimation of scour depth. Nowadays, machine learning techniques are being recognized as effective tools for the prediction of scour depth using experimental data. In the present study, gradient tree boosting (GTB) technique was used for the prediction of scour depth around various pier shapes under different streambed conditions. Sediment size, sediment quantity, velocity, and flow time were used as input parameters to predict the scour depth under clear-water and live-bed scour conditions. The scour depth was predicted for different pier shapes such as, circular, rectangular, round-nosed and sharp-nosed shaped. The GTB model predicted scour depth values were compared with that of the group method of data handling (GMDH) technique. The performance of GTB and GMDH models were then evaluated based on statistical indices such as RRMSE, NNSE, WI, MNE, SI, and KGE. The study concludes that the GTB model performance was relatively superior to that of GMDH in the prediction of scour depth around different pier shapes.

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“…They are used in the fields of prediction and classification, fields in which regression models and other related statistical techniques have traditionally been used [1][2][3][4]. Multilayer perceptron neural networks (MLPs) are one of the architectures of ANNs acting as a type of regression model, not necessarily parametric, which enables complex functional forms to be modeled [5,6].…”

Section: Introductionmentioning

confidence: 99%

Robustness of Imputation Methods with Backpropagation Algorithm in Nonlinear Multiple Regression

Hounmenou¹,

Behingan²,

Chrysostome³

et al. 2021

Preprint

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Missing observations constitute one of the most important issues in data analysis in applied research studies. The magnitude and their structure impact parameters estimation in the modeling with important consequences for decision-making. This study aims to evaluate the efficiency of imputation methods combined with the backpropagation algorithm in a nonlinear regression context. The evaluation is conducted through a simulation study including sample sizes (50, 100, 200, 300 and 400) with different missing data rates (10, 20, 30 40 and 50%) and three missingness mechanisms (MCAR, MAR and MNAR). Four imputation methods (Last Observation Carried Forward, Random Forest, Amelia and MICE) were used to impute datasets before making prediction with backpropagation. 3-MLP model was used by varying the activation functions (Logistic-Linear, Logistic-Exponential, TanH-Linear and TanH-Exponentiel), the number of nodes in the hidden layer (3 - 15) and the learning rate (20 - 70%). Analysis of the performance criteria (R2, r and RMSE) of the network revealed good performances when it is trained with TanH-Linear functions, 11 nodes in the hidden layer and a learning rate of 50%. MICE and Random Forest were the most appropriate for data imputation. These methods can support up to 50% of missing rate with an optimal sample size of 200.

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Prediction of pile group scour in waves using support vector machines and ANN

Cited by 40 publications

References 27 publications

Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods

Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods

Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers

Robustness of Imputation Methods with Backpropagation Algorithm in Nonlinear Multiple Regression

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