Prediction of Scour Depth below River Pipeline using Support Vector Machine

Parsaie, Abbas; Haghiabi, Amir Hamzeh; Moradinejad, Amir

doi:10.1007/s12205-019-1327-0

Cited by 42 publications

(13 citation statements)

References 39 publications

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“…They found superior outcomes of GP and ANN models over empirical equations. Parsaie et al [65] applied support vector machine (SVM), ANN, and ANFIS models to predict scour depth below the river pipeline system. Results of the comparison showed that a better prediction was achieved by SVM models (RMSE = 0.103 and R 2 = 0.94) over the ANN and ANFIS models.…”

Section: Scour Depth Prediction By Optimized Ann Modelsmentioning

confidence: 99%

Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway

et al. 2020

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A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway.

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Section: Scour Depth Prediction By Optimized Ann Modelsmentioning

confidence: 99%

Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway

et al. 2020

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“…Support Vector Machine (SVM) is another type of neuron-based model which was successfully applied in scour depth prediction. Parsaie et al (2019) observed that the SVM model has a higher prediction capability for scour depth prediction than ANN and ANFIS algorithm. Ahmad et al, (2018) revealed that SVM is sensitive to hyper-parameter selection, and Najafzadeh et al (2016) reported that ANFIS performed better than SVM and traditional existing equations.…”

Section: Introductionmentioning

confidence: 95%

A comparison between advanced hybrid machine learning algorithms and empirical equations applied to abutment scour depth prediction

Khosravi

Khozani

Mao

2021

Journal of Hydrology

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Complex vortex flow patterns around bridge piers, especially during floods, cause scour process that can result in the failure of foundations. Abutment scour is a complex three-dimensional phenomenon that is difficult to predict especially with traditional formulas obtained using empirical approaches such as regressions. This paper presents a test of a standalone Kstar model with five novel hybrid algorithm of bagging (BA-Kstar), dagging (DA-Kstar), random committee (RC-Kstar), random subspace (RS-Kstar), and weighted instance handler wrapper (WIHW-Kstar) to predict scour depth (ds) for clear water condition. The dataset consists of 99 scour depth data from flume experiments (Dey and Barbhuiya, 2005) using abutment shapes such as vertical, semicircular and 45° wing. Four dimensionless parameter of relative flow depth (h/l), excess abutment Froude number (Fe), relative sediment size (d50/l) and relative submergence (d50/h) were considered for the prediction of relative scour depth (ds/l). A portion of the dataset was used for the calibration (70%), and the remaining used for model validation.Pearson correlation coefficients helped deciding relevance of the input parameters combination and finally four different combinations of input parameters were used. The performance of the models was assessed visually and with quantitative metrics. Overall, the best input combination for vertical abutment shape is the combination of Fe, d50/l and h/l, while for semicircular and 45° wing the combination of the Fe and d50/l is the most effective input parameter combination. Our results show that incorporating Fe, d50/l and h/l lead to higher performance while involving d50/h reduced the models prediction power for vertical abutment shape and for semicircular and 45° wing involving h/l and d50/h lead to more error. The WIHW-Kstar provided the highest performance in scour depth prediction around vertical abutment shape while RC-Kstar model outperform of other models for scour depth prediction around semicircular and 45° wing.

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“…Ghazanfari Hashemi and Shahidi (2012) utilized SVM and ANN techniques to estimate pile groups scour, the results indicating that SVMs produce superior estimations of scour depth. Parsaie et al (2019) estimated the scour depth below a river pipeline by an SVM, comparing the results with those of ANNs and ANFISs, the SVM results being more accurate.…”

Section: Introductionmentioning

confidence: 99%

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

Sun

Bi²,

Karami

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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Accurate prediction of the scour hole depth and dimensions downstream of ski-jump spillways has been an important issue among hydraulic researchers for decades. In recent years, computing methods such as Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFISs) and Support Vector Regression (SVR) have shown a powerful performance in the prediction of scour characteristics owing to their flexibility and learning nature. In the present paper, a new hybrid approach has been proposed for the first time in order to improve the estimation power of the SVR tool for scour hole geometry prediction below ski-jump spillways. The principal characteristics of the scour hole pattern in the equilibrium phase have been predicted using SVR optimized with Fruitfly Optimization Algorithms (FOAs). The hybrid model is compared with the corresponding simple SVR model. To evaluate the proposed hybrid model further, it is also compared with other machine learning and empirical methods, such as ANNs, ANFISs and regression equations. The results show that the proposed SVR-FOA method performs well, improves remarkably on Support Vector Machines (SVMs) results, estimates scour hole geometrical parameters more accurately than the simple SVR model, and can be applied as an alternative reliable scheme for estimations on which simple SVR and other methods demonstrate shortcomings. The proposed hybrid method improves the precision level for scour depth prediction by about 8% compared with simple SVM in terms of the correlation coefficient.

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Prediction of Scour Depth below River Pipeline using Support Vector Machine

Cited by 42 publications

References 39 publications

Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway

Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway

A comparison between advanced hybrid machine learning algorithms and empirical equations applied to abutment scour depth prediction

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

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