Application of two different neural network techniques to lateral outflow over rectangular side weirs located on a straight channel

Journal of Hydrology and Hydromechanics

et al. 2016

A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers and drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment is estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equations do not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this study along with the multilayer perceptron (MLP), a hybrid method based on decision trees (DT) (MLP-DT), to estimate incipient motion. According to a comparison with the observed experimental outcome, the proposed method performs well (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = -0.036). The performance of MLP and MLP-DT is compared with that of existing regression-based equations, and significantly higher performance over existing models is observed. Finally, an explicit expression for practical engineering is also provided.

Section: Multi-layer Perceptron Neural Networkmentioning

confidence: 99%

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

Ebtehaj

Journal of Hydrology and Hydromechanics

et al. 2016

“…Soft computing methods have successfully been used in different areas of hydraulics (Bilhan, Emiroglu, & Kisi, 2010Chau, Wu, & Li, 2005;Dursun, Kaya, & Firat, 2012;Ebtehaj & Bonakdari, 2013;Jahangirzadeh et al, 2014;Kisi, Emiroglu, Bilhan, & Guven, 2012;Shamshirband et al, 2015;Wang, Chau, Cheng, & Qiu, 2009;Wang, Xu, Chau, & Lei, 2014;Wu & Chau, 2010;Wu, Chau, & Li, 2008;Yuhang and Wenxin, 2009). Sahu, Jana, Agarwal, and Khatua (2011) used artificial neural network (ANN) modeling to study and predict the velocity values within a meandering open channel.…”

Section: Introductionmentioning

confidence: 99%

Simulation of open channel bend characteristics using computational fluid dynamics and artificial neural networks

Gholami

Engineering Applications of Computational Fluid Mechanics

et al. 2015

The ability to simulate flow characteristics is one of the most important issues in the design and application of open channel bends. Three-dimensional computational fluid dynamics (CFD) and multi-layer feed-forward artificial neural networks (MLFF-ANNs) are used and compared in modeling the flow depth and velocity field in sharp bends. CFD is modeled in two phases, water and air, using the volume of fluid method. The backpropagation algorithm is applied in the training process of the ANN model. An experimental study of a 90°curved channel is undertaken to verify and compare the efficiency of the CFD and ANN models. The results show that both CFD and ANN methods can be successfully applied to the modeling of open channel bend characteristics. However, the ANN method performs significantly better than CFD.

“…The ability to predict the modified side weir discharge coefficient is significant in their design process. Soft computing methods are broadly used for the evaluation of modified side weir characteristics (Bilhan et al 2010;Kisi et al 2012;Onen 2014a, b).…”

Section: Introductionmentioning

confidence: 99%

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

2018

Appl Water Sci

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.