Journal Article

Application of Gaussian Process Regression Model to Predict Discharge Coefficient of Gated Piano Key Weir

Arvanaghi, Hadi, Farsadizadeh, Davood, Karbasi, Masoud, Salmasi, Farzin, Akbari, Masood

2019

Overview

The Piano Key (PK) weir is a new type of long crested weirs. This study was involved the addition of a gate to PK weir inlet keys. It was conducted by the Department of Water Engineering, University of Tabriz, Iran to determine if the gate increased hydraulic performance. A Gated Piano Key (GPK) weir was constructed and tested for discharge ranges of between 10 and 130 l per second. To this end, 156 experimental tests were performed and the effective parameters on the GPK weir discharge coefficient (Cd), such as gate dimensions (b and d), gate insertion depth in the inlet key (Hgate), the ratio of the inlet key width to the outlet key width (Wi/Wo) and the head over the GPK weir crest (H) were investigated. In addition, application of soft computing to estimate of Cd was carried out using MLP, GPR, SVM, GRNN, multiple linear and non-linear regressions methods using MATLAB 2018 software. This study suggests the relation for Cd with non-dimension parameters. The results of this study showed that H, Wi/Wo, Hgate and b and d, had the greatest effect on the GPK weir discharge coefficient, respectively. The GPR method was introduced as a new effective method for predicting discharge coefficient of weirs with RMSE = 0.011, R2 = 0.992 and MAPE = 1.167% and provided the best results when compared with other methods.

• Publisher: Springer Nature B.V
• Subject: Artificial intelligence / Dams / Dimensional analysis / Dimensions / Discharge coefficient / Gaussian process / Gene expression / Hydraulics / Inlets (waterways) / Investigations / Methods / Neural networks / Parameters / Piano / Regression analysis / Regression models / Soft computing / Support vector machines / Water engineering / Weirs / Width