Evaluation of side orifices shape factor using the novel approach self-adaptive extreme learning machine

Mahmodian, Ali Reza; Rajabi, Ahmad; Izadbakhsh, Mohammad Ali; Shabanlou, Saeid

doi:10.1007/s40808-019-00579-x

Cited by 6 publications

(1 citation statement)

References 20 publications

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“…Furthermore, More recently, the self-adaptive extreme learning machine (SAELM) as a novel ML approach has been employed to model the side weirs discharge on converging channels 50 and circular and rectangular side orifices along the open channel 51 – 53 . Jamei et al applied three data-driven approaches, multiple linear regression with interaction (MLRI), locally weighted learning regression (LWLR), and multiple linear regression (MLR), to estimate the discharge coefficient of a triangular side orifice.…”

Section: Introductionmentioning

confidence: 99%

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

Karbasi

Jamei

Ahmadianfar³

et al. 2021

Sci Rep

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In the present study, two kernel-based data-intelligence paradigms, namely, Gaussian Process Regression (GPR) and Kernel Extreme Learning Machine (KELM) along with Generalized Regression Neural Network (GRNN) and Response Surface Methodology (RSM), as the validated schemes, employed to precisely estimate the elliptical side orifice discharge coefficient in rectangular channels. A total of 588 laboratory data in various geometric and hydraulic conditions were used to develop the models. The discharge coefficient was considered as a function of five dimensionless hydraulically and geometrical variables. The results showed that the machine learning models used in this study had shown good performance compared to the regression-based relationships. Comparison between machine learning models showed that GPR (RMSE = 0.0081, R = 0.958, MAPE = 1.3242) and KELM (RMSE = 0.0082, R = 0.9564, MAPE = 1.3499) models provide higher accuracy. Base on the RSM model, a new practical equation was developed to predict the discharge coefficient. Also, the sensitivity analysis of the input parameters showed that the main channel width to orifice height ratio (B/b) has the most significant effect on determining the discharge coefficient. The leveraged approach was applied to identify outlier data and applicability domain.

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Section: Introductionmentioning

confidence: 99%