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

Akbari, Masood; Salmasi, Farzin; Arvanaghi, Hadi; Karbasi, Masoud; Farsadizadeh, D

doi:10.1007/s11269-019-02343-3

Cited by 56 publications

(15 citation statements)

References 28 publications

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“…Several prior research studies have used the GRNN method to estimate stream water temperature (Grbic et al 2013), soil temperature (Mihoub et al 2016a(Mihoub et al , 2016b, and GPK Weir discharge coefficients (Akbari et al 2019). The structure of the GRNN neural network is shown in Figure 4.…”

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

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Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

et al. 2021

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This study was designed to evaluate the ability of Artificial Intelligence (AI) methods including ANN, ANFIS, GRNN, SVM, GP, LR, and MLR to predict the relative energy dissipation(ΔE/Eu) for vertical drops equipped with a horizontal screen. For this study, 108 experiments were carried out to investigate energy dissipation. In the experiments, the discharge rate, drop height, and porosity of the screens were varied. Parameters yc/h, yd/yc, and p were input variables, and ΔE/Eu was the output variable. The efficiencies of the models were compared using the following metrics: correlation coefficient (CC), mean absolute error (MAE), root-mean-square error (RMSE), Normalized root mean square error (NRMSE) and Nash–Sutcliffe model efficiency (NSE). Results indicate that the performance of the ANFIS_gbellmf based model with a CC value of 0.9953, RMSE value of 0.0069, MAE value of 0.0042, NRMSE value as 0.0092 and NSE value as 0.9895 was superior to other applied models. Also, a linear regression yielded CC = 0.9933, RMSE = 0.0083, and MAE = 0.0067. This linear model outperformed multiple linear regression models. Results from a sensitivity study suggest that yc/h is the most effective parameter for predicting ΔE/Eu.

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Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

“…The basic aim of the GP technique is to prioritize the space functions (Mihoub et al 2016a(Mihoub et al , 2016b. The GP method has been used for diverse applications including the calculation of water temperatures (Grbic et al 2013) and the determination of weir discharge coefficients (Akbari et al 2019).…”

Section: Gaussian Process Model (Gp)mentioning

confidence: 99%

Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

et al. 2021

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“…Common association analysis methods include similarity analysis [ 4 , 5 ], cluster analysis [ 6 , 7 ], regression model [ 8 – 10 ], time series model [ 11 – 13 ], and artificial neural network (ANN) [ 14 – 16 ]. Similarity analysis and clustering analysis methods can effectively classify a variety of data, and these methods have been widely used in many fields [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm

et al. 2021

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Since water supply association analysis plays an important role in attribution analysis of water supply fluctuation, how to carry out effective association analysis has become a critical problem. However, the current techniques and methods used for association analysis are not very effective because they are based on continuous data. In general, there is different degrees of monotone relationship between continuous data, which makes the analysis results easily affected by monotone relationship. The multicollinearity between continuous data distorts these analytical methods and may generate incorrect results. Meanwhile, we cannot know the association rules and value interval between features and water supply. Therefore, the lack of an effective analysis method hinders the water supply association analysis. Association rules and value interval of features obtained from association analysis are helpful to grasp cause of water supply fluctuation and know the fluctuation interval of water supply, so as to provide better support for water supply dispatching. But the association rules and value interval between features and water supply are not fully understood. In this study, a data mining method coupling kmeans clustering discretization and apriori algorithm was proposed. The kmeans was used for data discretization to obtain the one-hot encoding that can be recognized by apriori, and the discretization can also avoid the influence of monotone relationship and multicollinearity on analysis results. All the rules eventually need to be validated in order to filter out spurious rules. The results show that the method in this study is an effective association analysis method. The method can not only obtain the valid strong association rules between features and water supply, but also understand whether the association relationship between features and water supply is direct or indirect. Meanwhile, the method can also obtain value interval of features, the association degree between features and confidence probability of rules.

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“…Artificial intelligence and machine learning models in different engineering problems such as hydraulic 24 – 26 , geotechnical 27 – 31 , and mechanical 32 engineerings have become very popular. In two recent decades, laboratory equipment and human errors, on the one hand, the complexity and nonlinear behavior of spatially varied flow through these facilities and the insufficient accuracy of classical regression-based methods, on the other hand, has caused several researchers to turn their attention to the data-driven and machine learning techniques 33 – 36 .…”

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

Cited by 56 publications

References 28 publications

Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm

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

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