An improved support vector regression model for estimation of saturation pressure of crude oils

Ansari, Hamid Reza; Gholami, Amin

doi:10.1016/j.fluid.2015.05.037

Cited by 40 publications

(11 citation statements)

References 78 publications

(74 reference statements)

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“…The kernel function of the SVM technique allows solving non-linear approximations into a linear function. The kernel functions used in this study were [69][70][71]:…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India

et al. 2020

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Modeling the stage-discharge relationship in river flow is crucial in controlling floods, planning sustainable development, managing water resources and economic development, and sustaining the ecosystem. In the present study, two data-driven techniques, namely wavelet-based artificial neural networks (WANN) and a support vector machine with linear and radial basis kernel functions (SVM-LF and SVM-RF), were employed for daily discharge (Q) estimation. The hydrological data of daily stage (H) and discharge (Q) from June to October for 10 years (2004–2013) at the Govindpur station, situated in the Burhabalang river basin, Orissa, were considered for analysis. For model construction, an optimum number of inputs (lags) was extracted using the partial autocorrelation function (PACF) at a 5% level of significance. The outcomes of the WANN, SVM-LF, and SVM-RF models were appraised over the observed value of Q based on performance indicators, viz., root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Pearson’s correlation coefficient (PCC), and Willmott index (WI), and through visual inspection (time variation, scatter plot, and Taylor diagram). Results of the evaluation showed that the SVM-RF model (RMSE = 104.426 m3/s, NSE = 0.925, PCC = 0.964, WI = 0.979) outperformed the WANN and SVM-LF models with the combination of three inputs, i.e., current stage, one-day antecedent stage, and discharge, during the testing period. In addition, the SVM-RF model was found to be more reliable and robust than the other models and having important implications for water resources management at the study site.

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“…The kernel function of the SVM technique allows solving non-linear approximations into a linear function. The kernel functions used in this study were [69][70][71]:…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India

et al. 2020

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“…Several studies have proven the excellent performance of the SVR method in solving petroleum‐related problems such as estimation of permeability, porosity, bubble point pressure, and so on. . However, in this paper, SVR is employed as a committee member for the quantitative estimation of wax deposition.…”

Section: Theory: Committee Machine With Ann and Svrmentioning

confidence: 99%

Combining of intelligent models through committee machine for estimation of wax deposition

Gholami

Ansari²,

Ahmadi

2018

J Chinese Chemical Soc

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Deposition of the wax is one of the thorny issues in the petroleum industry, invoking costly problems during the transportation and production of crude oil. Owing to its devastating impacts on oil companies' economy, it is essential to develop a simple and robust strategy for the quantitative estimation of wax deposition. In this paper, support vector regression (SVR) is first proposed to estimate the amount of wax deposition. Subsequently, an artificial neural network (ANN) is developed for wax deposition prediction. Eventually, a sophisticated committee machine (CM) is constructed for combining the results of the SVR and ANN models. Optimal contribution of each model in final prediction of the wax deposit is determined through genetic algorithm in CM. Statistical error analysis shows that the CM model performs better than the individual models performing alone.

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“…Therefore the positions, frequency and velocity of the bats are updated in each algorithm iteration. Updating process in order to find best solution is continues until a certain stop condition is met (Ansari and Gholami 2015). In this study, the stop condition is maximum number of iteration.…”

Section: Bat-inspired Algorithmmentioning

confidence: 99%

Fusing of optimized intelligence models by virtue of committee machine for estimation of the residual shear strength of clay

Gholami

Bodaghi

2016

Model. Earth Syst. Environ.

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Owing to high dependency of landslide stability to residual shear strength (RSS) of clay, provide a sophisticated strategy for modeling of this parameter is advantageous. This paper present strategy based upon fusing of optimized intelligence models for estimation of RSS of clay as a function of readily available data. The developed model is achieved through implementing two following steps. In the first step, two optimized models including optimized neural network, and optimized fuzzy logic are developed for estimation of RSS of clay. Optimizing method which implanted in predictive models for improving those performance is bat-inspired algorithm. In second step, committee machine (CM) is employed for combining outputs of aforementioned optimized models. Bat-inspired is incorporated in CM for determining optimal contribution of optimized elements in final prediction. The superior performance of CM rather than its elements is ascertain through those evaluation based on statistical criteria. Results of this study infer that proposed methodology provide an alternative way for making quantitative formulation between RSS of clay and its index properties.Keywords Residual shear strength (RSS) Á Optimized neural network (ONN) Á Optimized fuzzy logic (OFL) Á Committee machine (CM) Á Bat-inspired algorithm (BA)

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An improved support vector regression model for estimation of saturation pressure of crude oils

Cited by 40 publications

References 78 publications

Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India

Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India

Combining of intelligent models through committee machine for estimation of wax deposition

Fusing of optimized intelligence models by virtue of committee machine for estimation of the residual shear strength of clay

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