Genetic Programming to Predict Ski-Jump Bucket Spill-Way Scour

Azamathulla, Hazi Mohammad; Ghani, Aminuddin Ab.; Zakaria, Nor Azazi; Hin, Lai Sai; Chang, Chun Kiat; Leow, Cheng Siang; Abuhasan, Z.

doi:10.1016/s1001-6058(08)60083-9

Cited by 50 publications

(18 citation statements)

References 12 publications

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“…An alternative approach of GP was proposed in the estimation of relative scour depth using field data by [38]. The comparison between the GP model with ANN found that the GP model has good ability of forecasting the scour depth.…”

Section: Applications In Hydraulicsmentioning

confidence: 99%

Genetic Programming: A Novel Computing Approach in Modeling Water Flows

Londhe¹,

Dixit²

2012

Genetic Programming - New Approaches and Successful Applications

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Section: Applications In Hydraulicsmentioning

confidence: 99%

Genetic Programming: A Novel Computing Approach in Modeling Water Flows

Londhe¹,

Dixit²

2012

Genetic Programming - New Approaches and Successful Applications

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“…In the eld of mathematical modeling, using both of CFD and soft computing techniques was reported by Xiao et al [11].Nowadays, by advancing the soft computing techniques in the most areas related to hydraulic engineering, investigators have tried to use these techniques for predicting the scouring phenomena [12][13][14][15][16][17][18][19], speci cally scour depth at downstream of ip bucket. In this regard, using the Arti cial Neural Networks (ANNs), Genetic Programming (GP), Support Vector machine and M5 Model Tree, Group Method of Data Handling (GMDH), and Adaptive Neuro Fuzzy Inference System (ANFIS) can be mentioned [20][21][22][23][24][25][26][27][28][29][30][31][32]. Based on the reports, the precision of all the soft computing techniques was much more than the empirical formulas.…”

Section: Introductionmentioning

confidence: 99%

Estimation of scour downstream of a ski-jump bucket using the multivariate adaptive regression splines

Haghiabi

2017

Scientia Iranica

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Abstract. In this paper, modeling the scour downstream of a ip bucket of spillways was considered using empirical formulas, soft computing techniques such as multilayer perceptron (MLP) neural network, and Multivariate Adaptive Regression Splines (MARS). For this purpose, 95 data sets were collected with regard to the most a ective parameters on the scouring phenomena at downstream of spillways. During the MLP model development, it was found that the two transfer functions, such as log-sigmoid and radial basis, had very suitable performances for predicting the desired scouring phenomena. The results of MARS model showed that this model with coe cient of determination 0.99 and 0.91 during the development and testing stages, respectively, had suitable performance for modeling the scouring depth at downstream of ip bucket structure. The results of gamma test and MARS model indicated that q=(gd 3 w ), R=d w , and H=d w were the most a ective parameters on the scouring phenomena.

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“…As one of the valuable researches using genetic theory for ip buckets, it can be referred to studies of Azamthulla et al [8] who could successfully employ Genetic Programming (GP) in estimation of downstream scour depth. A general multi-objective optimization problem includes a series of objectives and it is related to a number of inequality and equality constraints.…”

Section: Introductionmentioning

confidence: 99%

The objective design of triangular bucket for dam's spillway using Non-dominated Sorting Genetic Algorithm II: NSGA-II

Hojjati

Neyshabouri

2017

Scientia Iranica

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Abstract. Ski jump is one of the most e ective structures in energy dissipation over spillways. Spillways have long been of practical importance to safety of dams. The major criteria in hydraulic design are based on the analytical and empirical methods. In the current study, in order to increase chute spillway e ciency, a multi-objective evolutionary algorithm known as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been utilized to design the optimal triangular bucket angle and chute width. In design method, two separate objective functions have been used. In the rst objective function, equations of dynamic pressure of the bucket, the jet length after bucket, and the scour depth have been used. The second objective function is related to construction volume of chute spillway. For calibrating the rst objective function, characteristics of Karoon III dam have been used as a case study. The di erence between design parameters of Karoon III spillway and those from NSGA-II algorithm method is less than 12 percent. According to the results, if the jet length is considered as the most impressive parameter in the rst objective function, design of the spillway becomes frugal.

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Genetic Programming to Predict Ski-Jump Bucket Spill-Way Scour

Cited by 50 publications

References 12 publications

Genetic Programming: A Novel Computing Approach in Modeling Water Flows

Genetic Programming: A Novel Computing Approach in Modeling Water Flows

Estimation of scour downstream of a ski-jump bucket using the multivariate adaptive regression splines

The objective design of triangular bucket for dam's spillway using Non-dominated Sorting Genetic Algorithm II: NSGA-II

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