Application of ANFIS–PSO Hybrid Algorithm for Predicting the Dimensions of the Downstream Scour Hole of Ski-Jump Spillways

Nou, Mohammad Rashki Ghaleh; Zolghadr, Masih; Bajestan, Mahmood Shafai; Azamathulla, Hazi Mohammad

doi:10.1007/s40996-020-00413-w

Cited by 12 publications

(2 citation statements)

References 37 publications

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“…As depicted in figure 10, the scour depth shows a positive correlation with the mean particle size (d 50 ), the diameter of the pile (D), Froude number (F r ) and current velocity (U m ). In order to obtain the optimized critical parameter values, various optimization techniques, like Particle Swarm Optimization [82,83], principal component analysis [84], ant colony optimization [85,86] and artificial bee colony [87][88][89], can be used in conjunction with the aforementioned ML algorithms to identify the critical parameters that significantly influence the scour depth to reduce the computation time and provide an improvement in the prediction. The correlation of all factors is shown in figure 10 using the absolute value of (r s ).…”

Section: (Vi) Comparison Of Machine-learning Model With Empirical Equ...mentioning

confidence: 99%

Physics-based and machine-learning models for accurate scour depth prediction

Jatoliya,

Bhattacharya,

Manna

et al. 2023

Phil. Trans. R. Soc. A.

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Scour phenomena remain a significant cause of instability in offshore structures. The present study estimates scour depths using physics-based numerical modelling and machine-learning (ML) algorithms. For the ML prediction, datasets were collected from previous studies, and the trained models checked against the statistical measures and reported outcomes. The numerical assessment of the scour depth has been also carried out for the current and coupled wave–current environment within a computational fluid dynamics framework with the aid of the open-source platform REEF3D. The outcomes are validated against the previously reported experimental studies. The results obtained from ML schemes demonstrated that the artificial neural network and adaptive neuro-fuzzy interface system models have an elevated level of effectiveness compared with the other models. Whereas the numerical analysis results show a good agreement against the reported values. For the current only conditions, the normalized scour depth ( S / D ) at the front and rear end of the pier is 0.65 and 0.81. For the wave–current conditions, the normalized scour depth ( S / D ) is 0.26. The study highlights the importance of machine learning and physics-based numerical modelling to assess scour depth within a reasonable time frame without compromising accuracy. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

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Section: (Vi) Comparison Of Machine-learning Model With Empirical Equ...mentioning

confidence: 99%

Physics-based and machine-learning models for accurate scour depth prediction

Jatoliya,

Bhattacharya,

Manna

et al. 2023

Phil. Trans. R. Soc. A.

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“…Scour is the process of water removing particles from the riverbed and banks of a river or channel. Many scholars have looked at the elements that influence this phenomenon to figure out what causes it and how to fix it [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Energy Dissipation in Ogee Spillway Using New Distributions of Dissipation Blocks

Al-Fatlawi,

Al-Shuker

2023

E3S Web Conf.

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Ogee spillway is the most important structure used to get rid of excess water during flood. Scouring phenomenon occurring in the downstream region due to high energy dissapation is an envitable. This study aims to reduce the amount of scour occurred at the downstream stilling basin of the ogee spillway by using three dissipation energy blocks namely as baffle, triangle and stepped blocks. A new distribution was proposed for each type of dissipated block, which varied between one row, two rows, and three rows. The flow and scour experimental tests were carried out under Froude numbers ranging from 2.5 to 8.5. During the tests, sequent depths and transverse velocities distribution were measured. The results showed that the used blocks appeared with good results in minimizing flow velocities and sediment scouring within the stilling basin in accordance with proposed distributions, especially with the range of Froude number 4.5-8.5. Also, The stepped block is characterized by reducing the flow velocities compared to the baffle block and triangle block, as it can be seen that there is a decrease in velocities up to 0.85% for all used distributions (A1, A2, A3, B1, B2, and C1).

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Improving Multivariate Runoff Prediction Through Multistage Novel Hybrid Models

Sibtain,

Li,

et al. 2024

Water Resour Manage

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The exploitation of hydropower provides cleaner, sustainable and cheaper energy contrary to fossil fuels.Therefore, hydropower offers prospects to meet the sustainable development goals of the United Nations.These bene ts motivate this study to develop different models for e cient runoff prediction utilizing multivariate hydro-meteorological data. The techniques employed for this purpose include correlation analysis, time series decomposition, sample entropy (SE), and sequence2sequence (S2S) algorithm with spatio-temporal attention (STAtt). The decomposition techniques include improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN) and the maxim overlap discrete wavelet transform (MODWT). The ICEEMDAN-STAtt-S2S model reveals the best prediction results over the counterpart hybrid and standalone models in terms of statistical metrics and comparison plots. The surpassed prediction outcomes substantiate the merger of ICEEMDAN and S2S utilizing STAtt for runoff prediction. Moreover, ICEEMDAN-STAtt-S2S offers the potential for reliable prediction of similar applications, including renewable energy, environment monitoring, and energy resources management.

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Application of ANFIS–PSO Hybrid Algorithm for Predicting the Dimensions of the Downstream Scour Hole of Ski-Jump Spillways

Cited by 12 publications

References 37 publications

Physics-based and machine-learning models for accurate scour depth prediction

Physics-based and machine-learning models for accurate scour depth prediction

Experimental Investigation of Energy Dissipation in Ogee Spillway Using New Distributions of Dissipation Blocks

Improving Multivariate Runoff Prediction Through Multistage Novel Hybrid Models

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