Use of artificial neural networks for prediction of discharge coefficient of triangular labyrinth side weir in curved channels

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

doi:10.1016/j.advengsoft.2011.02.006

Cited by 63 publications

(21 citation statements)

References 33 publications

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“…So, triangular, labyrinth and elliptical weirs can be used [7][8][9][10][11][12]. Such geometrical modifications can increase side weir discharge capacity by 1.5-4.5 times more than traditional rectangular ones [13,14].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of the discharge coefficient of a modified triangular side weir by adaptive neuro-fuzzy methodology

et al. 2015

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Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of the discharge coefficient of a modified triangular side weir by adaptive neuro-fuzzy methodology

et al. 2015

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“…Due to the high capacity of soft computing method to analyze complex problems, this method has been used extensively in various hydraulic engineering problems such as discharge capacity of lateral weirs (Neary, & Sotiropoulos, 1996;Bilhan, Emiroglu, & Kisi, 2011), scour depth prediction (Muzzammil, 2010), flow characteristics in different open channels (Donmez, 2001;Grace & Priest, 1958;More, 1978), rainfall modeling and stream flow prediction (Asadi, Shahrabi, Abbaszadeh, & Tabanmehr, 2013;Chau, Wu, & Li, 2005;Chen & Chau, 2006;Cheng, Lin, Sun, & Chau, 2005;Firat & Gungor, 2008;Wu, Chau, & Li, 2009;Yurtseven, & Zengin, 2013), modelling coastal algal blooms (Muttil & Chau, 2006), evapotranspiration (Cobaner, 2011;Kisi, & Ozturk, 2007), combined open channel flow (Hager, 1987), sediment transport (Ebtehaj, & Bonakdari, 2013;Van Maanen, Coco, Bryan, & Ruessink, 2010), ground water level prediction (Taormina, Chau, & Sethi, 2012) and water demand forecasting (Pulido-Calvo & Gutierrez-Estrada, 2009;Tiwari, & Adamowski, 2013).…”

Section: Introductionmentioning

confidence: 99%

Efficient methods for prediction of velocity fields in open channel junctions based on the artifical neural network

Zaji

Bonakdari

2015

Engineering Applications of Computational Fluid Mechanics

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Measurement results, sediment transport and pollutant dispersion in confluence regions are highly affected by flow structures of open channel junction and it is necessary to find an efficient method that can fully describe the velocity fields in junctions. In this study the velocity field in an open channel junction was investigated by using Artificial Neural Network (ANN) and three dimensional modelling. First, a modified Genetic Algorithm (GA) was introduced, then, an ANN model was optimized and the flow velocity was predicted in a 90 degree channel junction. Also, for three dimensional simulation of the free surface flow in the considered junction ANSYS-CFX software was used. Comparison of the results obtained from ANN and CFX models with laboratory data with root mean square error of 0.094 and 0.182 and percent standard error of prediction of − 9.75 and − 25.71 respectively, showed that the introduced ANN perform better than CFX in modelling velocity field in open channel junction and it was able to predict accurate in different areas and flow-rates.

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“…Many studies have been conducted to specify the characteristics of rectangular side weirs. However, besides the advantages of rectangular side weirs like easy construction, such side weirs have low efficiency (Bilhan et al 2011). In case of overflow, there are two choices: first, the length of the rectangular side weir can be increased, and second, side wire efficiency can be increased.…”

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.

show abstract

Use of artificial neural networks for prediction of discharge coefficient of triangular labyrinth side weir in curved channels

Cited by 63 publications

References 33 publications

Sensitivity analysis of the discharge coefficient of a modified triangular side weir by adaptive neuro-fuzzy methodology

Sensitivity analysis of the discharge coefficient of a modified triangular side weir by adaptive neuro-fuzzy methodology

Efficient methods for prediction of velocity fields in open channel junctions based on the artifical neural network

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

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