Prediction of discharge coefficient of side weir using adaptive neuro-fuzzy inference system

Abstract

Predicting the discharge coefficient of the hydraulic structures is one of the main subjects related to the hydro-system management. Weirs are the common hydraulic structure widely used in the water engineering projects. Side weir is the common type of hydraulic structure used in water engineering projects. Principal component analysis of the affective parameters on the side weir discharge coefficient leads to develop optimal structure for the empirical formulas and artificial intelligent models. In this paper, the principal component analysis (PCA) technique was used to define the most important affective parameters on the discharge coefficient of side weir (Cd sw ). The result of the PCA showed that the Froude number and ratio of the weir height to the upstream flow depth (P/h 1 ) are the most influential parameters affecting the Cd sw . Developing the adaptive neuro-fuzzy inference system (ANFIS) based on the PCA result showed that the optimal ANFIS structure is related to consider the five and four Gaussian membership function for the Froude number and P/h 1 parameters, respectively. The correlation coefficient of the ANFIS model during the training and testing stage was found to be 0.96 and 0.86 correspondingly.

Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms

“…Numerous attempts focus on the application of conventional artificial intelligence (AI), such as an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), to measure the coefficient of discharge through the side lateral weirs and side orifices. For instance, the mentioned approaches implemented to predict the discharge coefficient for rectangular sharp-crested weirs 37 – 40 ; for measuring the discharge capacity of rectangular sharp-crested side weirs in sewer systems 41 ; to assess the discharge coefficient of triangular and trapezoidal labyrinth side weirs; for estimating the discharge coefficient for a semi-elliptical labyrinth side weirs 42 ; to accurate determination of the discharge coefficient for a triangular side weir under subcritical flow conduction 26 , 43 , and predict the discharge of rectangular and circular side orifices in a rectangular channel 44 . The bedside, Gene expression programming (GEP) paradigm has been employed to determine the discharge coefficient of rectangular side weirs in various flow regimes along the rectangular and trapezoidal channels 45 , 46 .…”

Prediction of Undrained Shear Strength of Crushed Tire Mixture with Fine-Grained Soil by using Machine Learning Approaches

“…From last few decades, soft computing techniques such as artificial neural network (ANN), Gaussian process regression, Support vector machines (SVM)) and M5 model tree were successfully used in solution of various engineering related problems (Parsaie and Haghiabi 2016;Mohanty et al, 2019;Kumar and Sihag 2019;Al-Gabalawy et al, 2021 a and b;Salmasi et al, 2021;Sihag et al, 2020;Pandhiani et al, 2021;Bhoria et al, 2021, Thakur et al, 2021Sangeeta et al, 2021). Sihag et al, (2019) used ANFIS, SVM and random forest (RF) for the prediction of cumulative infiltration (CI) and infiltration rate (IR) in arid areas in Iran.…”

Estimation of scour downstream of a ski-jump bucket using the multivariate adaptive regression splines

“…Due to high cost of experiments and de ciency in laboratory studies due to simpli cations and limit range of measured parameters, researchers attempt to use the mathematical approaches for modeling and predicting the scour depth at downstream of ip buckets. In the eld of mathematical modeling, using both of CFD and soft computing techniques was reported by Xiao et al [11].Nowadays, by advancing the soft computing techniques in the most areas related to hydraulic engineering, investigators have tried to use these techniques for predicting the scouring phenomena [12][13][14][15][16][17][18][19], speci cally scour depth at downstream of ip bucket. In this regard, using the Arti cial Neural Networks (ANNs), Genetic Programming (GP), Support Vector machine and M5 Model Tree, Group Method of Data Handling (GMDH), and Adaptive Neuro Fuzzy Inference System (ANFIS) can be mentioned [20][21][22][23][24][25][26][27][28][29][30][31][32].…”