scholarly journals Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

2017 ◽  
Vol 62 (4) ◽  
pp. 825-841 ◽  
Author(s):  
J. Jakubowski ◽  
J.B. Stypulkowski ◽  
F.G. Bernardeau
Keyword(s):  
Linear Regression ◽  
Evaluation System ◽  
Regression Tree ◽  
Soft Rock ◽  
Multivariate Linear Regression ◽  
Pressure Balance ◽  
Tbm Performance ◽  
Regression Methods ◽  
Geotechnical Characteristics ◽  
Boring Machines

Abstract The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

scholarly journals Moisture Prediction from Simple Micrometeorological Data

1999 ◽  
Vol 89 (8) ◽  
pp. 668-672 ◽  
Author(s):  
Y. Chtioui ◽  
L. J. Francl ◽  
S. Panigrahi
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Flag Leaf ◽  
Linear Method ◽  
Regression Tree ◽  
Absolute Error ◽  
Generalized Regression Neural Network ◽  
Tree Model ◽  
Regression Methods ◽  
Plant Disease Management

Four linear regression methods and a generalized regression neural network (GRNN) were evaluated for estimation of moisture occurrence and duration at the flag leaf level of wheat. Moisture on a flat-plate resistance sensor was predicted by time, temperature, relative humidity, wind speed, solar radiation, and precipitation provided by an automated weather station. Dew onset was estimated by a classification regression tree model. The models were developed using micrometeorological data measured from 1993 to 1995 and tested on data from 1996 and 1997. The GRNN outperformed the linear regression methods in predicting moisture occurrence with and without dew estimation as well as in predicting duration of moisture periods. Average absolute error for prediction of moisture occurrence by GRNN was at least 31% smaller than that obtained by the linear regression methods. Moreover, the GRNN correctly predicted 92.7% of the moisture duration periods critical to disease development in the test data, while the best linear method correctly predicted only 86.6% for the same data. Temporal error distribution in prediction of moisture periods was more highly concentrated around the correct value for the GRNN than linear regression methods. Neural network technology is a promising tool for reasonably precise and accurate moisture monitoring in plant disease management.

scholarly journals Proposta de um modelo para predição do resultado das eleições presidenciais brasileiras baseado em técnicas de regressão

2021 ◽  
Author(s):  
Gabriel Robaina ◽  
Fabiano Baldo
Keyword(s):  
Linear Regression ◽  
Prediction Models ◽  
Regression Tree ◽  
Multivariate Linear Regression ◽  
Prediction Errors ◽  
Popular Vote ◽  
Wide Availability ◽  
Vote Intention ◽  
Tree Models ◽  
Political Economic

Prediction of elections is a subject that excites the population, especially in the last few months before an election. In Brazil, there is a wide availability of political, economic and social data, in institutions such as TSE, IBGE and opinion research institutes that can be used as sources to create prediction models. Therefore, this work aims to build multivariate linear regression and regression tree models to predict the percentage of votes received by the situational candidate for the presidency of Brazil. The multivariate linear regression model had the smallest prediction errors, with MAE of 1.45 in the first round and 1.48 in the second, with margins smaller than 1\% in 2002, 2006 and 2018. The proposed models seemed to be more accurate than other models found in the literature. As main contributions, it was possible to observe that the sampling of data by state and the use of the illiteracy rate and the popular vote intention contributed directly to the performance of the models.

Comparison of multivariate linear regression methods in micro-Raman spectrometric quantitative characterization

2015 ◽  
Vol 46 (6) ◽  
pp. 566-576 ◽  
Author(s):  
Attila Farkas ◽  
Balázs Vajna ◽  
Péter L. Sóti ◽  
Zsombor K. Nagy ◽  
Hajnalka Pataki ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multivariate Linear Regression ◽  
Quantitative Characterization ◽  
Regression Methods

PENGARUH FAKTOR-FAKTOR INTERNAL PERBANKAN TERHADAP NON PERFORMING FINANCING PADA BANK UMUM SYARIAH

2019 ◽  
Vol 6 (2) ◽  
pp. 115-130
Author(s):  
Eka Ambara Harci Putranta ◽  
Lilik Ambarwati
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Capital Adequacy ◽  
Annual Reports ◽  
Regression Methods ◽  
Capital Adequacy Ratio ◽  
Negative Effect ◽  
Independent Variable

The study aims to analyze the influence of internal banking factors in the form of: Capital Adequency Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing at Sharia Banks. This research method used multiple linear regression analysis with the help of SPSS 16.00 software which is used to see the influence between the independent variables in the form of Capital Adequacy Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing. The sample of this study was 3 Islamic Commercial Banks, so there were 36 annual reports obtained through purposive sampling, then analyzed using multiple linear regression methods. The results showed that based on the F Test, the independent variable had an effect on the NPF, indicated by the F value of 17,016 and significance of 0,000, overall the independent variable was able to explain the effect of 69.60%. While based on the partial t test, showed that CAR has a significant negative effect, Total assets have a significant positive effect with a significance value below 0.05 (5%). Meanwhile FDR does not affect NPF.

scholarly journals Association between triglyceride-glucose index and risk of arterial stiffness: a cohort study

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Shouling Wu ◽  
Luli Xu ◽  
Mingyang Wu ◽  
Shuohua Chen ◽  
Youjie Wang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Linear Regression ◽  
Arterial Stiffness ◽  
Multivariate Linear Regression ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Cross Sectional ◽  
Tyg Index ◽  
Longitudinal Association ◽  
The Cross

Abstract Background Triglyceride–glucose (TyG) index, a simple surrogate marker of insulin resistance, has been reported to be associated with arterial stiffness. However, previous studies were limited by the cross-sectional design. The purpose of this study was to explore the longitudinal association between TyG index and progression of arterial stiffness. Methods A total of 6028 participants were derived from the Kailuan study. TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Arterial stiffness was measured using brachial-ankle pulse wave velocity (baPWV). Arterial stiffness progression was assessed by the annual growth rate of repeatedly measured baPWV. Multivariate linear regression models were used to estimate the cross-sectional association of TyG index with baPWV, and Cox proportional hazard models were used to investigate the longitudinal association between TyG index and the risk of arterial stiffness. Results Multivariate linear regression analyses showed that each one unit increase in the TyG index was associated with a 39 cm/s increment (95%CI, 29–48 cm/s, P < 0.001) in baseline baPWV and a 0.29 percent/year increment (95%CI, 0.17–0.42 percent/year, P < 0.001) in the annual growth rate of baPWV. During 26,839 person-years of follow-up, there were 883 incident cases with arterial stiffness. Participants in the highest quartile of TyG index had a 58% higher risk of arterial stiffness (HR, 1.58; 95%CI, 1.25–2.01, P < 0.001), as compared with those in the lowest quartile of TyG index. Additionally, restricted cubic spline analysis showed a significant dose–response relationship between TyG index and the risk of arterial stiffness (P non-linearity = 0.005). Conclusion Participants with a higher TyG index were more likely to have a higher risk of arterial stiffness. Subjects with a higher TyG index should be aware of the following risk of arterial stiffness progression, so as to establish lifestyle changes at an early stage.

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