Predicting discharge coefficient of compound broad-crested weir by using genetic programming (GP) and artificial neural network (ANN) techniques

Salmasi, Farzin; Yıldırım, Gürol; Masoodi, Azam; Parsamehr, P

doi:10.1007/s12517-012-0540-7

Cited by 44 publications

(10 citation statements)

References 12 publications

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“…The genetic programming (GP), initially presented by Koza (1992), is an optimal method to find an analytical formula to a problem via artificial intelligence algorithms. GP is widely used by researchers to solve various geotechnical problems (Javadi et al 2006;Karakus 2011;Shen et al 2012;Salmasi et al 2013;Alazba et al 2016;Rahgoshay et al 2019;Uysal 2020;Farrokhi et al 2020). Figure 4 shows a formula of (x-y)/cos (z) generated in GP analyses, which is expressed as a tree structure containing terminals (x, y, z) and functions (-, / and cos).…”

Section: Overview Of Genetic Programmingmentioning

confidence: 99%

Predicting the shear strength of saw-tooth jointed rocks using genetic programming

et al. 2021

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The geometrical and mechanical properties of joints play an important role in the determination of shear strength of jointed rocks. In this research, we propose a nonlinear model to estimate the shear strength of jointed rocks with unfilled saw-tooth triangular asperities using the basic friction angle φ b , the tensile strength of intact rock σ t , and the joint inclination angle i under various normal stress σ n conditions. In order to propose the new shear strength model, firstly, a rock database of saw-tooth jointed rocks containing experimental and numerical data is established. The experimental data are collected from publications. The numerical data are generated by the particle flow code (PFC) model. Then, the genetic programming (GP) is used to analyze the database to propose the GP shear strength model. The prediction performance of the proposed GP model is tested and compared with that of the Patton model and the Ladanyi and Archambault (LA) model. Comparison results show that the proposed GP model has the best prediction performance compared with the Patton model and the LA model. The proposed GP model can capture the nonlinear shear strength behavior of saw-tooth jointed rocks and its input parameters can be easily estimated.

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Section: Overview Of Genetic Programmingmentioning

confidence: 99%

Predicting the shear strength of saw-tooth jointed rocks using genetic programming

et al. 2021

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“…Haykin (2010) has provided a detailed theoretical description of ANN. However, ANN is easy to use and it can approximate any input/output map (Salmasi et al 2013).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

et al. 2021

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This study was designed to evaluate the ability of Artificial Intelligence (AI) methods including ANN, ANFIS, GRNN, SVM, GP, LR, and MLR to predict the relative energy dissipation(ΔE/Eu) for vertical drops equipped with a horizontal screen. For this study, 108 experiments were carried out to investigate energy dissipation. In the experiments, the discharge rate, drop height, and porosity of the screens were varied. Parameters yc/h, yd/yc, and p were input variables, and ΔE/Eu was the output variable. The efficiencies of the models were compared using the following metrics: correlation coefficient (CC), mean absolute error (MAE), root-mean-square error (RMSE), Normalized root mean square error (NRMSE) and Nash–Sutcliffe model efficiency (NSE). Results indicate that the performance of the ANFIS_gbellmf based model with a CC value of 0.9953, RMSE value of 0.0069, MAE value of 0.0042, NRMSE value as 0.0092 and NSE value as 0.9895 was superior to other applied models. Also, a linear regression yielded CC = 0.9933, RMSE = 0.0083, and MAE = 0.0067. This linear model outperformed multiple linear regression models. Results from a sensitivity study suggest that yc/h is the most effective parameter for predicting ΔE/Eu.

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“…Ghani, 2018;Wu & Chau, 2011;Yaseen, El-Shafie, Afan, Hameed, Wan Mohtar & Hussain, 2016;Yaseen, Sulaiman, Deo, & Chau, 2019;Zounemat-Kermani, Beheshti, Ataie-Ashtiani, & Sabbagh-Yazdi, 2009). These artificial intelligence methods benefit from the simplicity, ability, and proper results of modern and advanced computers (Nguyen, Ahn, & Park, 2018;Salmasi, Yıldırım, Masoodi, & Parsamehr, 2013), knowledge based and data based nature, predictions without need of model form, and greater endurance over the data errors (Azamathulla, 2005;Bateni & Jeng, 2007) than statistical models. For instance, Liriano and Day (2001) reported higher performance of the ANN method in estimating scour dimension near a culvert in comparison to the existing empirical equations.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

Riahi-Madvar

Dehghani

Seifi

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and location of the scour pit. The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. Four non-dimensional geometry parameters of scour hole shape are predicted by these models including the maximum scour depth (S), the distance of S from the weir (XS), the maximum height of downstream deposited sediments (hd), and distance of hd from the weir (XD). The best results over train data derived for XS/Z and hd/Z by the MLP model with R 2 are 0.95 and 0.96 respectively; the best predictions for S/Z and XD/Z are from the ANFIS model with R 2 0.91 and 0.96 respectively. The results indicate that the application of MLP and ANFIS results in the accurate prediction of scour geometry for the designing of stable grade control structures in alluvial irrigation channels.

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Predicting discharge coefficient of compound broad-crested weir by using genetic programming (GP) and artificial neural network (ANN) techniques

Cited by 44 publications

References 12 publications

Predicting the shear strength of saw-tooth jointed rocks using genetic programming

Predicting the shear strength of saw-tooth jointed rocks using genetic programming

Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

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