Assessing heuristic models through k-fold testing approach for estimating scour characteristics in environmental friendly structures

Abdollahpour, Milad; Dalir, A Hosseinzadeh; Farsadizadeh, D; Shiri, Jalal

doi:10.1080/09715010.2017.1408041

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Hence, researchers investigate other fast, easy, and accurate methods for scouring estimation in hydraulic studies. Recently, hydraulic experts employ soft computing techniques in estimating scouring in many studies (Muzzammil 2008;Guven et al 2009;Adarsh 2010;Ebtehaj and Bonakdari 2013;Rikar et al 2016;Najafzadeh et al 2017;Parsaie et al 2018;Abdollahpour et al 2019). The main goal of prediction with AI techniques was following the recent developments to obtain the best model performances (Khatibi et al 2017).…”

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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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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“…Artificial neural networks (ANN) is a branch of artificial intelligence (AI), which are capable of perceiving complex nonlinear behavior of phenomena [6]. This approach, which is inspired by human brains' functions, is employed in prediction studies of several hydraulic parameters [14][15][16][17][18][19][20]. Moreover, it has been used in the estimation of equilibrium bridge pier scour effectively [21,22].…”

Section: Introductionmentioning

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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“…In recent years, the potential of SC models for prediction of scouring depth downstream of weirs have been assessed using different models like genetic expression programming, self-adaptive extreme learning machine and neuro-fuzzy [7,[34][35][36]. The research findings indicated that the SC models provided better performance in comparison with regression formulas.…”

Section: Introductionmentioning

confidence: 99%

Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models

et al. 2020

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Considering the scouring depth downstream of weirs is a challenging issue due to its effect on weir stability. The adaptive neuro-fuzzy inference systems (ANFIS) model integrated with optimization methods namely cultural algorithm, biogeography based optimization (BBO), invasive weed optimization (IWO) and teaching learning based optimization (TLBO) are proposed to predict the maximum depth of scouring based on the different input combinations. Several performance indices and graphical evaluators are employed to estimate the prediction accuracy in the training and testing phase. Results show that the ANFIS-IWO offers the highest prediction performance (RMSE = 0.148) compared to other models in the testing phase, while the ANFIS-BBO (RMSE = 0.411) provides the lowest accuracy. The findings obtained from the uncertainty analysis of prediction modeling indicate that the input variables variability has a higher impact on the predicted results than the structure of models. In general, the ANFIS-IWO can be used as a reliable and cost-effective method for predicting the scouring depth downstream of weirs.

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Assessing heuristic models through k-fold testing approach for estimating scour characteristics in environmental friendly structures

Cited by 5 publications

References 18 publications

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

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

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

Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models

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