Prediction of Local Scour Depth Downstream of Sluice Gates Using Harmony Search Algorithm and Artificial Neural Networks

Bashiri, Hamid; Sharifi, Erfaneh; Singh, Vijay P.

doi:10.1061/(asce)ir.1943-4774.0001305

Cited by 15 publications

(4 citation statements)

References 26 publications

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“…On the other hand, the HSA was applied in Bashiri et al ( 2018 ) in which the authors applied a parameter varying method to increase the ability of the HSA. The results demonstrated that coupling an ANN coupled with the HSA is an accurate and simple method for predicting the maximum scour depth downstream of sluice gates.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

Alweshah

2022

Soft Comput

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Classification is a technique in data mining that is used to predict the value of a categorical variable and to produce input data and datasets of varying values. The classification algorithm makes use of the training datasets to build a model which can be used for allocating unclassified records to a defined class. In this paper, the coronavirus herd immunity optimizer (CHIO) algorithm is used to boost the efficiency of the probabilistic neural network (PNN) when solving classification problems. First, the PNN produces a random initial solution and submits it to the CHIO, which then attempts to refine the PNN weights. This is accomplished by the management of random phases and the effective identification of a search space that can probably decide the optimal value. The proposed CHIO-PNN approach was applied to 11 benchmark datasets to assess its classification accuracy, and its results were compared with those of the PNN and three methods in the literature, the firefly algorithm, African buffalo algorithm, and β-hill climbing. The results showed that the CHIO-PNN achieved an overall classification rate of 90.3% on all datasets, at a faster convergence speed as compared outperforming all the methods in the literature. Supplementary Information The online version contains supplementary material available at 10.1007/s00500-022-06917-z.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

Alweshah

2022

Soft Comput

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“…The traditional reliance on empirical formulae and physical models has encountered challenges due to their inherent constraints. ML has emerged as a promising solution (Bashiri et al 2018). ML algorithms are designed to identify patterns and relationships from large datasets, making them suitable candidates for tasks that require prediction based on complex interdependent variables.…”

Section: Introductionmentioning

confidence: 99%

Predicting maximum scour depth at sluice outlet: a comparative study of machine learning models and empirical equations

Le,

Thu Hien

2024

Environ. Res. Commun.

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Estimating the maximum scour depth of sluice outlets is pivotal in hydrological engineering, directly influencing the safety and efficiency of water infrastructure. This research compared traditional empirical formulas with advanced machine learning (ML) algorithms, including RID, SVM, CAT, and XGB, utilizing experimental datasets from prior studies. Performance statistics highlighted the efficacy of the ML algorithms over empirical formulas, with CAT and XGB leading the way. Specifically, XGB demonstrated superiority with a correlation coefficient (CORR) of 0.944 and a root mean square error (RMSE) of 0.439. Following closely, the CAT model achieved a CORR of 0.940, and SVM achieved 0.898. For empirical formulas, although CORR values up to 0.816 and RMSE values of 0.799 can be obtained, these numbers are still lower than most ML algorithms. Furthermore, a sensitivity analysis underscored the densimetric Froude number (Fd) as the most crucial factor in ML models, with influences ranging from 0.839 in RID to 0.627 in SVM. Uncertainty in ML model estimates was further quantified using the Monte Carlo technique with 1,000 simulations on testing datasets. CAT and XGB have shown more stability than the other models in providing estimates with mean CORRs of 0.937 and 0.946, respectively. Their 95% confidence intervals (CIs) are [0.929–0.944] for CAT and [0.933–0.954] for XGB. These results demonstrated the potential of ML algorithms, particularly CAT and XGB, in predicting the maximum scour depth. Although these models offer high accuracy and higher 95% CI than others, the empirical formulas retain their relevance due to their simplicity and quick computation, which may still make them favored in certain scenarios.

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“…Aamir and Ahmad [17,18] investigated experimentally the characteristics of submerged jets causing scour downstream of an apron and developed an empirical equation for the prediction of equilibrium scour depth. Recently, researchers used soft-computing techniques to predict scour downstream of sluice gates and other hydraulic structures [19][20][21][22][23][24]. Aamir and Ahmad [25] put forth a review of scour under plane two-dimensional wall jets.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Rough Rigid Apron on Scour Downstream of Sluice Gates

Aamir

Ahmad

Pandey

et al. 2022

Water

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Presented in this paper is an experimental study on scour downstream of rough and smooth stiff aprons under the influence of wall jets. The effect of various parameters on asymptotic scour depth is studied. The analysis of data reveals that maximum scour depth reduces with increasing sediment size, sluice opening, and apron length. It is directly proportional to densimetric Froude number. Maximum equilibrium scour depth initially decreases with increasing tailwater level (up to a minimum), but thereafter an increase is observed. The effect of roughness of stiff apron on scour is studied and quantification of decline in maximum scour depth due to roughness is carried out. Scour profiles and their temporal variation are also studied. A regression-based equation to predict equilibrium scour depth under smooth and rough apron is proposed, which conforms to experimental data. Based on the conclusions of this study, it is recommended to use roughness over the surface of stiff apron to confine scour under-wall jets.

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Prediction of Local Scour Depth Downstream of Sluice Gates Using Harmony Search Algorithm and Artificial Neural Networks

Cited by 15 publications

References 26 publications

RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

Predicting maximum scour depth at sluice outlet: a comparative study of machine learning models and empirical equations

The Effect of Rough Rigid Apron on Scour Downstream of Sluice Gates

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