Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches

Kisi, Özgür; Emiroğlu, M. Emin; Bilhan, Omer; Güven, Aytaç

doi:10.1016/j.eswa.2011.09.035

Cited by 61 publications

(30 citation statements)

References 21 publications

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“…In the computational hydraulic field, the water surface profile and flow properties were studied (Parsaie and Haghiabi 2015a, b). Side weir discharge coefficient was predicted and modeled by most types of neural network techniques such as multilayer perceptron (MLP) neural network, adaptive neuro-fuzzy inference system (ANFIS), and group method of data handling (GMDH) (Ebtehaj et al 2015a;Emiroglu et al 2011b;Kisi et al 2012). Based on the reports the accuracy of these models are much more than the empirical formulas.…”

Section: Introductionmentioning

confidence: 99%

Prediction of discharge coefficient of side weir using adaptive neuro-fuzzy inference system

Parsaie

Haghiabi

2016

Sustain. Water Resour. Manag.

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Predicting the discharge coefficient of the hydraulic structures is one of the main subjects related to the hydro-system management. Weirs are the common hydraulic structure widely used in the water engineering projects. Side weir is the common type of hydraulic structure used in water engineering projects. Principal component analysis of the affective parameters on the side weir discharge coefficient leads to develop optimal structure for the empirical formulas and artificial intelligent models. In this paper, the principal component analysis (PCA) technique was used to define the most important affective parameters on the discharge coefficient of side weir (Cd sw ). The result of the PCA showed that the Froude number and ratio of the weir height to the upstream flow depth (P/h 1 ) are the most influential parameters affecting the Cd sw . Developing the adaptive neuro-fuzzy inference system (ANFIS) based on the PCA result showed that the optimal ANFIS structure is related to consider the five and four Gaussian membership function for the Froude number and P/h 1 parameters, respectively. The correlation coefficient of the ANFIS model during the training and testing stage was found to be 0.96 and 0.86 correspondingly.

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Section: Introductionmentioning

confidence: 99%

Prediction of discharge coefficient of side weir using adaptive neuro-fuzzy inference system

Parsaie

Haghiabi

2016

Sustain. Water Resour. Manag.

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“…Many direct and indirect methods have been used to calculate the shear stress along a wetted Some researchers utilized GEP model to solve different hydraulic problems [21][22][23]. Azamathulla and Zahiri [24] used linear genetic programming to predict the ow discharge in a compound open channel.…”

Section: Introductionmentioning

confidence: 99%

Mean bed shear stress estimation in a rough rectangular channel using a hybrid genetic algorithm based on an artificial neural network and genetic programming

2017

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Abstract. The determination of erosion and deposition patterns in channels requires detailed knowledge and estimation of the bed shear stress. In this investigation, the application of a Genetic Algorithm-based Arti cial (GAA) neural network and Genetic Programming (GP) was presented to predict bed shear stress in a rectangular channel with rough boundaries. Several input combinations, tness functions, and transfer functions were investigated to determine the best GAA model. Also, the e ect of various GP operators on estimating bed shear stress was studied. A comparison between the GAA and GP techniques' abilities to predict bed shear stress was made and then investigated. The results revealed that the GAA model performs better in predicting the bed shear stress (RMSE = 0.0774), as compared to the GP model (RMSE = 0.0835).

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“…The ability to analyse complex problems has popularized soft computing methods in different sciences, particularly water engineering, e.g., flood forecasting (Chau, Wu, & Li, 2005;Wu, Chau, & Li, 2008), longitudinal dispersion coefficient in rivers (Mohamed & Hashem, 2006), model manipulation for hydrological processes , forecasting daily and monthly discharge (Cheng, Chau, Sun, & Lin, 2005;Taormina & Chau, 2015;Wang, Chau, Cheng, & Qiu, 2009;Wu, Chau, & Li, 2009), rainfall and runoff (Chau & Wu, 2010;Wang, Chau, Xu, & Chen, 2015), lateral outflow over side weirs (Bilhan, Emiroglu, & Kisi, 2010;Kisi, Emiroglu, Bilhan, & Guven, 2012), velocity field simulation (Bonakdari, Baghalian, Nazari, & Fazli, 2011;Gholami, Bonakdari, Zaji, & Akhtari, 2015), velocity field simulation in junctions (Zaji & Bonakdari, 2015a), discharge coefficient estimation (Dursun, Kaya, & Firat, 2012) and sediment transport (Ebtehaj & Bonakdari, 2013). A multi-layer perceptron (MLP) model is a type of artificial neural network (ANN) used to predict variables.…”

Section: Introductionmentioning

confidence: 99%

Design of modified structure multi-layer perceptron networks based on decision trees for the prediction of flow parameters in 90° open-channel bends

Gholami

Bonakdari

Zaji

et al. 2016

Engineering Applications of Computational Fluid Mechanics

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A modified multi-layer perceptron (MLP) model based on decision trees (DT-MLP) is presented to predict velocity and water free-surface profiles in a 90°open-channel bend. The ability of the new hybrid model to predict the velocity and flow depth in a 90°sharp bend is investigated and compared with the abilities of MLP and multiple-linear regression (MLR) models. The MLP and DT-MLP networks are trained and tested using 520 and 506 experimental data measured for velocity and flow depth, respectively, at five different discharge rates of 5, 7.8, 13.6, 19.1 and 25.3 l/s. The MLP and DT-MLP comparison results against MLR reveal that the two artificial neural networks (ANNs) are 84% and 16% more accurate than the MLR model in predicting the velocity and flow depth variables, respectively. According to the results, the root mean square error (RMSE) value of the DT-MLP model decreases by 9% and 7.5% in predicting velocity and flow depth, respectively, compared with the MLP model. It was found that the hybrid decision-tree-based method can significantly improve MLP neural network performance in forecasting velocity and free-surface profiles in a 90°open-channel bend. ARTICLE HISTORY

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Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches

Cited by 61 publications

References 21 publications

Prediction of discharge coefficient of side weir using adaptive neuro-fuzzy inference system

Prediction of discharge coefficient of side weir using adaptive neuro-fuzzy inference system

Mean bed shear stress estimation in a rough rectangular channel using a hybrid genetic algorithm based on an artificial neural network and genetic programming

Design of modified structure multi-layer perceptron networks based on decision trees for the prediction of flow parameters in 90° open-channel bends

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