Modeling discharge coefficient of normal and inverted orientation labyrinth weirs using machine learning techniques. Issue 3 (2nd September 2017)

• Record Type: Journal Article
• Authors: Roushangar, Kiyoumars; Alami, Mohammad Taghi; Majedi Asl, Mahdi; Shiri, Jalal
• Is Part Of: ISH journal of hydraulic engineering. Volume 23:Issue 3(2017)
• Page Start: 331
• Page End: 340
• Publication Date: 2017-09-02
• Subjects: ANFIS -- GEP -- inverted orientation -- labyrinth weirs -- normal orientation; Hydraulic engineering -- Periodicals; Hydraulic engineering -- India -- Periodicals; Hydraulic engineering; India; Periodicals; 627
• DOI: 10.1080/09715010.2017.1327333
• Languages: English
• ISSNs: 0971-5010

Abstract

Abstract: A labyrinth weir is a polygonal overflow weir that is characterized by its hydraulic performance and distinct geometric shape (triangular, trapezoidal or rectangular cycles). The present study provides new knowledge and design information on the performance and operation of adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) techniques for predicting discharge coefficient of labyrinth weirs in a flume. Discharge coefficient, C d, was considered as a function of headwater ratio, upstream Froude number, magnification ratio, sidewall angle, apex ratio, and cycle width ratio (dimensionless parameters). The records obtained through 300 laboratory data sets were used to determine the C d of labyrinth weirs. The results showed that the best GEP model determined the C d values of the normal and inverted orientation labyrinth weir with a RMSE of 0.0139, 0.590, DC of 0.977, 0.852 and R of 0.996, 0.923, respectively by using the four dimensionless parameters of Fr, H T / P, L c / W and A / w as input variables. The best ANFIS model determined the C d value of the normal orientation labyrinth weir by using all six dimensionless parameters (RMSE = 0.0110, DC = 0.964, R = 0.987) and inverted orientation labyrinth weir by using three dimensionless parameters of H T / P, α, and w / p as input variables (RMSE = 0.0290, DC = 0.952, R = 0.987). The results of sensitivity analysis showed that Fr in the GEP model and Fr and H T / P in the ANFIS model are the most effective variables for determining C d for normal and inverted orientation labyrinth weirs, respectively. It was found that when low headwater ratios were eliminated from analysis, performance accuracy of the models was improved.