Hybrid model of support vector regression and fruitfly optimization algorithm for predicting ski-jump spillway scour geometry

Sun, Xiaoyin; Bi, Yi; Karami, Hojat; Naini, Shayan; Band, Shahab S.; Mosavi, Amir

doi:10.1080/19942060.2020.1869102

Cited by 19 publications

(7 citation statements)

References 28 publications

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“…SVM can be used in classifier and regressor problems. A regressionbased SVM is usually called an SVR and the main aim of an SVM is to minimize the structural risk for solving complex problems (Samadianfard et al, 2019;Sun et al, 2021). One of the advantages of this algorithm is that it does not fall into the trap of local optimizations owing to the use of global optimization methods in its structure.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

Karami

Rezaei

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely the adaptive neural-fuzzy inference system, the support vector machine, the M5 model tree, the least-squares support vector machine and the least-squares support vector machine-bat algorithm (LSSVM-BA). A total of 355 empirical data for 6 different congressional overflow models were extracted from the results of a laboratory study on labyrinth overflow models. The parameters of the upstream water head to overflow ratio, the lateral wall angle and the curvature angle were used to estimate the discharge coefficient of curved labyrinth overflows. Based on various statistical evaluation indicators, the results show that those input parameters can be relied upon to predict the discharge coefficient. Specifically, the LSSVM-BA model showed the best prediction accuracy during the training and test phases. Such a low-cost prediction model may have a remarkable practical implication as it could be an economic alternative to the expensive laboratory solution, which is costly and time-consuming.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…It achieved a high convergence rate. Sun et al (2021) utilized SVR optimized with fruitfly optimization algorithms (FOAs) to predict the scour hole pattern in the equilibrium phase. Other researchers applied the sunflower optimization (SO) algorithm with ANFIS and ANN for lake water level simulation.…”

Section: Introductionmentioning

confidence: 99%

Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

Karami

Rezaei

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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“…Parsaie et al (2019) used the SVM, ANN, and ANFIS for the scour estimation of a pipeline in the river, and the results were satisfactory. Sun et al (2021) used a hybrid method combining the fruit fly optimization algorithm (FOA) with the SVR for predicting the scour features downstream of ski-jump spillways. The results indicated that the FOA-SVR method remarkably improved the results and outperformed the regression models.…”

Section: -Introductionmentioning

confidence: 99%

Comparative study of soft computing models for prediction of scour below two symmetric crossing jets

2022

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The scour estimation downstream of the dam's hydraulic structures is a serious issue and has long been considered an important topic by hydraulic engineers. The literature shows that the computational methods are good alternatives for the prediction of the scour in various hydraulic structures in which the conventional methods demonstrate shortcomings. In the present paper, the different machine learning techniques have been used for the first time to predict the scour below the two symmetric crossing jets. Four different artificial neural networks (ANNs), including feed forward back propagation (FFBP), cascade forward back propagation (CFBP), radial basis function (RBF), generalized regression neural network (GRNN) along with the adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR) were employed and evaluated. Two important scour hole characteristics, namely the maximum scour hole depth, and its location relative to the scour hole origin, are predicted at different crossing angles. The soft computing models are also compared with the traditional empirical methods. The results indicate that the applied computing techniques perform satisfactorily and improve the results remarkably. They can estimate the scour more precisely than the regression models and can be considered robust alternative tools. A detailed sensitivity analysis is also performed on the FFBP that shows the crossing angle parameter has a powerful effect on the predictions. The SVR improved the results to 47% and 31.71% for the scour hole depth and its location in terms of the coefficient of correlation.

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“…However, due to many uncertainties in ANN modeling techniques, many attempts have been made by researchers to improve the model efficiency by applying optimization algorithms or developing other AI methods. The application of support vector regression (SVR) model in scour hole modeling has been significantly used in recent times (e.g., Goyal and Ojha 2011;Sharafi et al 2016;Hoang et al 2018;Sun et al 2021). It is imperative to note that the dimensionality of the input space in the SVR model does not affect the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Hybrid model of support vector regression and innovative gunner optimization algorithm for estimating ski-jump spillway scour depth

et al. 2022

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Scour hole that occurs downstream of the hydraulic structures threatens the safety and stability of the hydraulic structures. The scour around the structures is a complex and important hydraulic phenomenon; hence, it requires a data extensive research for the accurate estimation of scour depth. Although many analytical models are available for scour depth estimation, they suffer from huge limitations. In this research, the support vector regression (SVR) model and SVR ensemble with the metaheuristic algorithm of innovative gunner (SVR-AIG) models have been developed for accurate prediction of scour depth downstream of the ski-jump spillways. Field measurements including head and discharge intensity are used for developing the models. The performances of the models are compared using root mean square error (RMSE), mean average error (MAE), and correlation coefficient (CC) criteria and some statistical plots. The results showed that the hybrid SVR-AIG-based estimations (with CC = 0.987, 0.991, RMSE = 2.839, 1.987, and MAE = 2.247, 1.201) are more accurate than the SVR standalone model estimations (with CC = 0.942, 0.975, RMSE = 5.686, 4.040, and MAE = 4.114, 3.201) at the training and testing phases. This study is an important reference for analyzing the high capability of the AIG as an optimization tool in improving scour estimations of a standalone model. Also, this algorithm eliminates the trial-and-error procedure to optimize the internal parameters during the model development. Graphical abstract

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Hybrid model of support vector regression and fruitfly optimization algorithm for predicting ski-jump spillway scour geometry

Cited by 19 publications

References 28 publications

Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

Comparative study of soft computing models for prediction of scour below two symmetric crossing jets

Hybrid model of support vector regression and innovative gunner optimization algorithm for estimating ski-jump spillway scour depth

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