Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multi-layer moored floating pipe breakwater

Patil, S.G.; Mandal, S.; Hegde, Arkal Vittal

doi:10.1016/j.advengsoft.2011.09.026

Cited by 45 publications

(18 citation statements)

References 41 publications

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“…Support vector machines (SVM) applied as regression is a soft computing tool developed within a statistical learning theory by concerning various error optimization stages [22,28]. Despite the prosperous performance of standard SVM, it has some shortcomings.…”

Section: Least Squares Support Vector Machinesmentioning

confidence: 99%

“…As result of comparisons that has been conducted in their study, the superiority of SVM has been emphasized. In the literature, this method is also applied to the areas of coastal engineering, such as prediction of wave transmission over a submerged reef [18], damage level prediction of non-reshaped berm breakwater [19][20][21], and wave transmission of floating pipe breakwater [22].…”

Section: Introductionmentioning

confidence: 99%

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Least Squares Support Vector Mechanics to Predict the Stability Number of Rubble-Mound Breakwaters

Gedik

2018

Water

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In coastal engineering, empirical formulas grounded on experimental works regarding the stability of breakwaters have been developed. In recent years, soft computing tools such as artificial neural networks and fuzzy models have started to be employed to diminish the time and cost spent in these mentioned experimental works. To predict the stability number of rubble-mound breakwaters, the least squares version of support vector machines (LSSVM) method is used because it can be assessed as an alternative one to diverse soft computing techniques. The LSSVM models have been operated through the selected seven parameters, which are determined by Mallows’ Cp approach, that are, namely, breakwater permeability, damage level, wave number, slope angle, water depth, significant wave heights in front of the structure, and peak wave period. The performances of the LSSVM models have shown superior accuracy (correlation coefficients (CC) of 0.997) than that of artificial neural networks (ANN), fuzzy logic (FL), and genetic programming (GP), that are all implemented in the related literature. As a result, it is thought that this study will provide a practical way for readers to estimate the stability number of rubble-mound breakwaters with more accuracy.

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Section: Least Squares Support Vector Machinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Least Squares Support Vector Mechanics to Predict the Stability Number of Rubble-Mound Breakwaters

Gedik

2018

Water

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“…Due to the recognized limitations of these models, data driven methods such as Support vector Regression (SVR) models have been widely used in recent times for forecasting in many areas of science and engineering. There are vast surge of reports on application of SVR in marine related field such as significant wave height prediction or wave parameters studies [3,4,6,10,14]. Support vector Regression (SVR), a novel data-driven and artificial intelligence-based model, shows remarkable prediction performances for nonlinear systems in many disciplines [5,12].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Significant Wave Height Using Support Vector Regression

Ajeesh

Deka

2015

2015 Fifth International Conference on Advances in Computing and Communications (ICACC)

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The reliability of wave prediction is a crucial issue in coastal, harbor and ocean engineering. Support vector machine (SVM) is an appropriate and suitable method for significant wave height (Hs) prediction due to its best versatility, robustness, and effectiveness. In this present work, only significant wave height (Hs) of previous time steps were used as predictors during the period 01-01-2004 to 01-04-2004. The data used is processed significant wave height (Hs) of the station SW4(Latitude 12056′31″ and longitude 74043′58″) located near west coast of India.70% of the data used for calibration of model parameters and remaining 30% data used for validation using various input combinations. The performance of both the RBF and PUK models is assessed using different statistical indices. (E.g. CC (RBF-SVR) = 0.82, CC (PUK-SVR) = 0.93; MAE (RBF-SVR) = 0.04, MAE (PUK-SVR) =0.04 RMSE (RBF-SVR) =0.06, RMSE (PUK-SVR) =0.05.The results show that SVM can be successfully used for prediction of Hs.

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“…As an alternative to ANN, an SVM was developed that could effectively classify data and minimize the risk [11]. Unlike ANN, which operates based on the minimization of the training error, the SVM was created based on the minimization of the upper bound of the generalization error, which summarized the training error and confidential term [12]. The aim of the SVM method is to find the global optimum rather than local optima by solving the nonlinear problem in a high-dimensional region.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Concrete Strength with P-, S-, R-Wave Velocities by Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Park

Yoon

2019

Applied Sciences

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Mechanical waves, such as ultrasonic waves, have shown promise for use in non-destructive methods used in the evaluation of concrete properties, such as strength and elasticity. However, accurate estimation of the concrete compressive strength is difficult if only the pressure waves (P-waves) are considered, which is common in non-destructive methods. P-waves cannot reflect various factors such as the types of aggregates and cement, the fine aggregate modulus, and the interfacial transition zone, influencing the concrete strength. In this study, shear waves (S-waves) and Rayleigh waves (R-waves) were additionally used to obtain a more accurate prediction of the concrete strength. The velocities of three types of mechanical waves were measured by recent ultrasonic testing methods. Two machine learning models—a support vector machine (SVM) and an artificial neural network (ANN)—were developed within the MATLAB programming environment. Both models were successfully used to model the relationship between the mechanical wave velocities and the concrete compressive strength. The machine learning model that included the P-, S-, and R-wave velocities was more accurate than the model that included only the P-wave velocity.

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Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multi-layer moored floating pipe breakwater

Cited by 45 publications

References 41 publications

Least Squares Support Vector Mechanics to Predict the Stability Number of Rubble-Mound Breakwaters

Least Squares Support Vector Mechanics to Predict the Stability Number of Rubble-Mound Breakwaters

Forecasting of Significant Wave Height Using Support Vector Regression

Prediction of Concrete Strength with P-, S-, R-Wave Velocities by Support Vector Machine (SVM) and Artificial Neural Network (ANN)

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