Length prediction of non-aerated region flow at baffled chutes using intelligent nonlinear regression methods

Baffled chutes are used in irrigation systems, storm water systems, wastewater canal chutes, river training, and drop structures for energy dissipation. Two flow regions occur on the flow surface of baffled chutes. These are black water and white water regions. Knowing the location of the inception point where white water begins to appear on the surface is important for determination of the non-aerated flow region. Thus, cavitation damage can be prevented. In this study, 160 laboratory test results have been used for determining black water length (i.e., length of the non-aerated region) of baffled chutes with stepped, wedge, trapezoidal, and T-shaped baffle blocks. The obtained observation data have been analyzed by well-known soft computing methods such as artificial neural networks (ANN), curve fitting (CF), non-linear regression (NLR) and special extreme learning machine (ELM). The methods’ performance in mapping input data to the output were compared. The mean regression errors calculated by the curve fitting model, ANN, NLR and ELM are obtained as 2.5, 8.0, 11.25 and 0.8 %, respectively. The experimental results show that ELM’s nonlinear system modeling capability is superior to ANN, NLR, and CF.

[1]  P. B. Deolalikar,et al.  Neural Networks for Estimation of Scour Downstream of a Ski-Jump Bucket , 2005 .

[2]  Fi-John Chang,et al.  Modelling combined open channel flow by artificial neural networks , 2005 .

[3]  Hubert Chanson,et al.  Comparison of energy dissipation between nappe and skimming flow regimes on stepped chutes , 1994 .

[4]  R. M. Khatsuria.  Hydraulics of Spillways and Energy Dissipators , 2004 .

[5]  Anna Papst,et al.  Hydraulic Design Of Stilling Basins And Energy Dissipators , 2016 .

[6]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[7]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[8]  Babak Omidvar,et al.  Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic , 2010, Expert Syst. Appl..

[9]  Nihat Kaya,et al.  Study of oxygen transfer efficiency at baffled chutes , 2010 .

[10]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[11]  Carlos Javier Mantas,et al.  A generic fuzzy aggregation operator: rules extraction from and insertion into artificial neural networks , 2007, Soft Comput..

[12]  Muhammed Fatih Talu,et al.  Calculation of melatonin and resveratrol effects on steatosis hepatis using soft computing methods , 2013, Comput. Methods Programs Biomed..

[13]  Özgür Kisi,et al.  Predicting discharge capacity of triangular labyrinth side weir located on a straight channel by using an adaptive neuro-fuzzy technique , 2010, Adv. Eng. Softw..

[14]  Li-Chiu Chang,et al.  Intelligent control for modelling of real‐time reservoir operation , 2001 .

[15]  Abdulkadir Sengur,et al.  GA-SELM: Greedy algorithms for sparse extreme learning machine , 2014 .

[16]  Nachimuthu Karunanithi,et al.  Neural Networks for River Flow Prediction , 1994 .

[17]  Sang-Woo Ji,et al.  Efficiency assessment of cascade aerator in a passive treatment system for Fe(II) oxidation in ferruginous mine drainage of net alkaline , 2015, Environmental Earth Sciences.

[18]  Zhiqiang Deng,et al.  How Reliable Are ANN, ANFIS, and SVM Techniques for Predicting Longitudinal Dispersion Coefficient in Natural Rivers? , 2016 .

[19]  Li-Chiu Chang,et al.  Intelligent control for modeling of real‐time reservoir operation, part II: artificial neural network with operating rule curves , 2005 .

[20]  Özgür Kisi,et al.  Application of two different neural network techniques to lateral outflow over rectangular side weirs located on a straight channel , 2010, Adv. Eng. Softw..

[21]  H. K. Zare,et al.  Inception Point of Air Entrainment and Training Wall Characteristics of Baffles and Sills on Stepped Spillways , 2012 .

[22]  Davut Hanbay,et al.  Predicting flow conditions over stepped chutes based on ANFIS , 2009, Soft Comput..

[23]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[24]  Hamid Mehdizadeh,et al.  A framework development for predicting the longitudinal dispersion coefficient in natural streams using an artificial neural network , 2011 .

[25]  Willi H. Hager,et al.  Two-Phase Flow Characteristics of Stepped Spillways , 2003 .

[26]  Ah Chung Tsoi,et al.  On the distribution of performance from multiple neural-network trials , 1997, IEEE Trans. Neural Networks.

[27]  Farhad Hooshyaripor,et al.  Effective prediction of scour downstream of ski-jump buckets using artificial neural networks , 2014, Water Resources.

[28]  P. B. Deolalikar,et al.  Estimation of scour below spillways using neural networks , 2006 .