Self-adaptive extreme learning machine-based prediction of roller length of hydraulic jump on rough bed

Heydari, Majeid; Shabanlou, Saeid; Sanahmadi, Babak

doi:10.1080/09715010.2020.1852978

ISH Journal of Hydraulic Engineering

2020

DOI: 10.1080/09715010.2020.1852978

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Self-adaptive extreme learning machine-based prediction of roller length of hydraulic jump on rough bed

Majeid Heydari

Saeid Shabanlou

Babak Sanahmadi

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2024

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Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions

Abbaszadeh,

Daneshfaraz,

Sume

et al. 2024

AQUA — Water Infrastructure, Ecosystems and Society

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This investigation focuses on flow energy, a crucial parameter in the design of water structures such as channels. The research endeavors to explore the relative energy loss (ΔEAB/EA) in a constricted flow path of varying widths, employing Support Vector Machine (SVM), Artificial Neural Network (ANN), Gene Expression Programming (GEP), Multiple Adaptive Regression Splines (MARS), M5 and Random Forest (RF) models. Experiments span a Froude number range from 2.85 to 8.85. The experimental findings indicate that the ΔEAB/EA exceeds that observed in a classical hydraulic jump with constriction section. Within the SVM model, the linear kernel emerges as the best predictor of ΔEAB/EA, outperforming polynomial, radial basis function (RBF), and sigmoid kernels. In addition, in the ANN model, the MLP network was more accurate compared to the RBF network. The results indicate that the relationship proposed by the MARS model can play a significant role resulting in high accuracy compared to the non-linear regression relationship in predicting the target parameter. Upon comprehensive evaluation, the ANN method emerges as the most promising among the candidates, yielding superior performance compared to the other models. The testing phase results for the ANN-MLP are noteworthy, with R = 0.997, average RE% = 0.63%, RMSE = 0.0069, BIAS = −0.0004, DR = 0.999, SI = 0.0098 and KGE = 0.995.

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Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions

Abbaszadeh,

Daneshfaraz,

Sume

et al. 2024

AQUA — Water Infrastructure, Ecosystems and Society

View full text Add to dashboard Cite

show abstract

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Self-adaptive extreme learning machine-based prediction of roller length of hydraulic jump on rough bed

Cited by 1 publication

References 40 publications

Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions

Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions

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