Estimating discharge coefficient of semi-elliptical side weir using ANFIS

Dursun, O. Faruk; Kaya, Nihat; Fırat, Mahmut

doi:10.1016/j.jhydrol.2012.01.010

Cited by 65 publications

(31 citation statements)

References 36 publications

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“…Therefore, the data set was divided into four training and testing sub-sets by using the cross validation method as a systematic methodology to obtain effective and sensitive model findings. To get more reliable evaluations of performance of the ANFIS model, MLR given in Equation (16) was established for the 108 experimental data [32]. As with the fuzzy approach, the four-fold cross validation method was used again.…”

Section: Applicationsmentioning

confidence: 99%

Computing Air Demand Using the Takagi–Sugeno Model for Dam Outlets

Zounemat‐Kermani

Scholz

2013

Water

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An adaptive neuro-fuzzy inference system (ANFIS) was developed using the subtractive clustering technique to study the air demand in low-level outlet works. The ANFIS model was employed to calculate vent air discharge in different gate openings for an embankment dam. A hybrid learning algorithm obtained from combining back-propagation and least square estimate was adopted to identify linear and non-linear parameters in the ANFIS model. Empirical relationships based on the experimental information obtained from physical models were applied to 108 experimental data points to obtain more reliable evaluations. The feed-forward Levenberg-Marquardt neural network (LMNN) and multiple linear regression (MLR) models were also built using the same data to compare model performances with each other. The results indicated that the fuzzy rule-based model performed better than the LMNN and MLR models, in terms of the simulation performance criteria established, as the root mean square error, the Nash-Sutcliffe efficiency, the correlation coefficient and the Bias.

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Section: Applicationsmentioning

confidence: 99%

Computing Air Demand Using the Takagi–Sugeno Model for Dam Outlets

Zounemat‐Kermani

Scholz

2013

Water

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“…Dursun et al (2012) applied ANFIS to predict the characteristics of semi-elliptical side weirs. Kisi et al (2013) simulated the discharge coefficient of rectangular side weirs using ANFIS and concluded that ANFIS performs much better…”

Section: Introductionmentioning

confidence: 99%

New type side weir discharge coefficient simulation using three novel hybrid adaptive neuro-fuzzy inference systems

Bonakdari

Zaji

2018

Appl Water Sci

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In many hydraulic structures, side weirs have a critical role. Accurately predicting the discharge coefficient is one of the most important stages in the side weir design process. In the present paper, a new high efficient side weir is investigated. To simulate the discharge coefficient of these side weirs, three novel soft computing methods are used. The process includes modeling the discharge coefficient with the hybrid Adaptive Neuro-Fuzzy Interface System (ANFIS) and three optimization algorithms, namely Differential Evaluation (ANFIS-DE), Genetic Algorithm (ANFIS-GA) and Particle Swarm Optimization (ANFIS-PSO). In addition, sensitivity analysis is done to find the most efficient input variables for modeling the discharge coefficient of these types of side weirs. According to the results, the ANFIS method has higher performance when using simpler input variables. In addition, the ANFIS-DE with RMSE of 0.077 has higher performance than the ANFIS-GA and ANFIS-PSO methods with RMSE of 0.079 and 0.096, respectively.

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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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Estimating discharge coefficient of semi-elliptical side weir using ANFIS

Cited by 65 publications

References 36 publications

Computing Air Demand Using the Takagi–Sugeno Model for Dam Outlets

Computing Air Demand Using the Takagi–Sugeno Model for Dam Outlets

New type side weir discharge coefficient simulation using three novel hybrid adaptive neuro-fuzzy inference systems

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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