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coefficient 13

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Theoretical prediction and validation of global horizontal solar irradiance for a tropical climate in India

Sivasankari SUNDARAM,Jakka SARAT CHANDRA BABU

Frontiers in Energy 2015, Volume 9, Issue 3,   Pages 311-321 doi: 10.1007/s11708-015-0369-3

Abstract: The proposed regression models were validated by the significance of statistical indicators such as mean

Keywords： global horizontal irradiance (GHI)     mean bias error     root mean square error     mean percentage error     coefficientof regression     Visual Basic 2010 Express    

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Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

Frontiers in Energy 2013, Volume 7, Issue 1,   Pages 103-110 doi: 10.1007/s11708-012-0220-z

Abstract: Then on the basis of the simulated results, the multiple regression models were developed respectivelyAccording to the analysis of regression coefficients, the appropriate building envelope design schemesAt last, the regression model evaluations consisting of the simulation evaluations and the actual caseevaluations were performed to verify the feasibility and accuracy of the regression models.It is believed that the regression models developed in this paper can be used to estimate the energy

Keywords： regression model     energy consumption     building envelope     office building     different climates    

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Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 221-236 doi: 10.1007/s11705-021-2061-y

Abstract: Two approaches can be adopted in the estimation of the model parameters: stepwise regression, used inseveral of previous publications, and Lasso regression, which is newly incorporated in this paper forTherefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety

Keywords： data-driven modeling     pharmaceutical organic synthesis     Lasso regression     dynamic response surface methodology    

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Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 5,   Pages 1181-1198 doi: 10.1007/s11709-021-0744-6

Abstract: The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificial

Keywords： interaction     load sharing ratio     piled raft     nonlinear regression     artificial neural network    

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Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 103-109 doi: 10.1007/s11709-018-0474-6

Abstract: Therefore, this paper presents a non-linear regression model for the analysis of effect of embedmentThrough multiple iterations, the value of coefficient of determination is found to reach 0.987.

Keywords： settlement     embedment     Fox depth correction factor     regression     multivariable    

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RETRACTED ARTICLE: Momentum exchange coefficient for two jet flows mixing in a tee junction

Frontiers of Chemical Science and Engineering 2023, Volume 17, Issue 12,   Pages 2161-2161 doi: 10.1007/s11705-009-0143-3

Abstract: RETRACTED ARTICLE: Momentum exchange coefficient for two jet flows mixing in a tee junction

Keywords： tee junction     Momentum exchange coefficient     RETRACTED    

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of driver-response relationships: identifying factors using a novel framework integrating quantile regression

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1676-2

Abstract:

● A novel framework integrating quantile regression with machine learning

Keywords： Driver-response     Upper boundary of relationship     Interpretable machine learning     Quantile regression    

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compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression

Ali Reza GHANIZADEH, Morteza RAHROVAN

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 4,   Pages 787-799 doi: 10.1007/s11709-019-0516-8

Abstract: compressive strength (UCS) of soil-RAP blend stabilized with Portland cement based on multivariate adaptive regression

Keywords： soil-reclaimed asphalt pavement blend     Portland cement     unconfined compressive strength     multivariate adaptive regression    

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Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 2,   Pages 133-136 doi: 10.1007/s11709-013-0202-1

Abstract: This article examines the capability of Gaussian process regression (GPR) for prediction of effective

Keywords： unsaturated soil     effective stress parameter     Gaussian process regression (GPR)     artificial neural network    

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Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

Frontiers of Structural and Civil Engineering 2017, Volume 11, Issue 1,   Pages 90-99 doi: 10.1007/s11709-016-0363-9

Abstract: In this paper, considering the experimental results, three different models of multiple linear regressionevaluate the compressive strength of concrete with different mix designs, however, multiple linear regression

Keywords： concrete     28 days compressive strength     multiple linear regression     artificial neural network     ANFIS     sensitivity    

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Experimental study of heat transfer coefficient with rectangular baffle fin of solar air heater

Foued CHABANE,Nesrine HATRAF,Noureddine MOUMMI

Frontiers in Energy 2014, Volume 8, Issue 2,   Pages 160-172 doi: 10.1007/s11708-014-0321-y

Abstract: The heat transfer coefficient between the absorber plate and air can be considerably increased by using

Keywords： Nusselt number     flow rate     heat transfer     heat transfer coefficient     thermal efficiency     forced convection    

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SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 1,   Pages 185-198 doi: 10.1007/s11709-019-0591-x

Abstract: With this study, along with the conventional methods of simple and multiple linear regression models,and their performances are evaluated in terms of the resulting root mean squared error ( ) values and coefficient

Keywords： undrained shear strength     linear regression     random forest     gradient boosting     machine learning     standard    

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A photolysis coefficient for characterizing the response of aqueous constituents to photolysis

David R. HOKANSON,Ke LI,R. Rhodes TRUSSELL

Frontiers of Environmental Science & Engineering 2016, Volume 10, Issue 3,   Pages 428-437 doi: 10.1007/s11783-015-0780-3

Abstract: Quantum yield ( ) and molar absorption coefficient ( ) are the two critical parameters measuring theIt was shown that this product, named the photolysis coefficient, , can be determined using standardThe development showed the photolysis coefficient to be equally useful for microbiological, inorganicValues of calculated by the authors based on quantum yield and molar absorption coefficient data from

Keywords： UV photolysis     disinfection     advanced oxidation     N-nitrosodimethylamine     quantum yield     absorption coefficient    

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Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 250-265 doi: 10.1007/s11709-021-0785-x

Abstract: Regression analysis was then applied to determine the relationship between SMS, flow rate, andBy the use of regression analysis, the mathematical relationships between variables in all facilities

Keywords： pedestrian density     regression analysis     GP model     GMDH model    

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Identifying factors that influence soil heavy metals by using categorical regression analysis: A case

Jun Yang, Jingyun Wang, Pengwei Qiao, Yuanming Zheng, Junxing Yang, Tongbin Chen, Mei Lei, Xiaoming Wan, Xiaoyong Zhou

Frontiers of Environmental Science & Engineering 2020, Volume 14, Issue 3, doi: 10.1007/s11783-019-1216-2

Abstract: In this study, a categorical regression was used to identify the factors that influence soil heavy metalsinfluence of different factors on the soil heavy metal contents in Beijing was analyzed using a categorical regressionA categorical regression represents a suitable method for identifying the factors that influence soil

Keywords： Soil     Heavy metal     Influencing factor     Categorical regression     Identification method    

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Title Author Date Type Operation

Theoretical prediction and validation of global horizontal solar irradiance for a tropical climate in India

Sivasankari SUNDARAM,Jakka SARAT CHANDRA BABU

Journal Article

Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

Journal Article

Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Journal Article

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

Journal Article

Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Journal Article

RETRACTED ARTICLE: Momentum exchange coefficient for two jet flows mixing in a tee junction

Journal Article

of driver-response relationships: identifying factors using a novel framework integrating quantile regression

Journal Article

compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression

Ali Reza GHANIZADEH, Morteza RAHROVAN

Journal Article

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Journal Article

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

Journal Article

Experimental study of heat transfer coefficient with rectangular baffle fin of solar air heater

Foued CHABANE,Nesrine HATRAF,Noureddine MOUMMI

Journal Article

SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Journal Article

A photolysis coefficient for characterizing the response of aqueous constituents to photolysis

David R. HOKANSON,Ke LI,R. Rhodes TRUSSELL

Journal Article

Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

Journal Article

Identifying factors that influence soil heavy metals by using categorical regression analysis: A case

Jun Yang, Jingyun Wang, Pengwei Qiao, Yuanming Zheng, Junxing Yang, Tongbin Chen, Mei Lei, Xiaoming Wan, Xiaoyong Zhou

Journal Article