scholarly journals Application of modern methods: modeling of sedimentary soil ESP content

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Linear Regression ◽  
Fuzzy Inference ◽  
Evaluation Criteria ◽  
Exchange Capacity ◽  
Linear Regression Method ◽  
Linear Regression Models ◽  
Sodic Soils ◽  
Connection Weight ◽  
Inference Systems ◽  
Clay Percentage

Knowing the exchangeable sodium percentage (ESP) variations and its values in sodic or saline-sodic soils is essential in order to estimate the amount of soil amendments and better land management. ESP calculated from cation exchange capacity (CEC), and since CEC measurement is difficult and time-consuming, ESP computation is costly and subject to error. Thus, presenting a method to estimate ESP indirectly, by an easily available index is much more efficient and economical. In this study, 296 soil samples collected and analyzed from Sistan plain, southeastern Iran. Soil ESP were predicted by using artificial neural networks, comprising radial basis functions (RBFN) and multilayer perceptron (MLP) and adaptive neuro-fuzzy inference systems (ANFIS), and results compared with stepwise linear regression method. Results indicated that the linear regression models performed poorly in order to estimate ESP (R2 ≤ 0.58 and root mean square error (RMSE) ≥ 4.31). Applying fewer inputs (electrical conductivity (EC) and pH), ANFIS showed better results (R2=0.80, RMSE=2.34), while increasing inputs (clay and organic carbon) decreased the accuracy (R2=0.82, RMSE=4.20). Using more inputs, RBFN resulted in better performance in comparison with other methods (R2=0.83, RMSE=2.85). Sensitivity analysis using the connection weight method demonstrated that EC, pH, clay percentage and bulk density are the most important variables in order to explain ESP variability in the region, respectively. Generally, considering the evaluation criteria and the number of used variables of models, ANFIS (with EC and pH as inputs) is the most appropriate method for estimating ESP in Sistan plain.

scholarly journals Application of Modern Methods: Modeling of Sedimentary Soil ESP Content

2019 ◽  
Vol 1 (1) ◽  
pp. 1-17
Author(s):  
Sorush Niknamian
Keyword(s):  
Linear Regression ◽  
Fuzzy Inference ◽  
Evaluation Criteria ◽  
Exchange Capacity ◽  
Linear Regression Method ◽  
Linear Regression Models ◽  
Sodic Soils ◽  
Connection Weight ◽  
Inference Systems ◽  
Clay Percentage

Knowing the exchangeable sodium percentage (ESP) variations and its values in sodic or saline-sodic soils is essential in order to estimate the amount of soil amendments and better land management. ESP calculated from cation exchange capacity (CEC), and since CEC measurement is difficult and time-consuming, ESP computation is costly and subject to error. Thus, presenting a method to estimate ESP indirectly, by an easily available index is much more efficient and economical. In this study, 296 soil samples collected and analyzed from Sistan plain, southeastern Iran. Soil ESP were predicted by using artificial neural networks, comprising radial basis functions (RBFN) and multilayer perceptron (MLP) and adaptive neuro-fuzzy inference systems (ANFIS), and results compared with stepwise linear regression method. Results indicated that the linear regression models performed poorly in order to estimate ESP (R2 ≤ 0.58 and root mean square error (RMSE) ≥ 4.31). Applying fewer inputs (electrical conductivity (EC) and pH), ANFIS showed better results (R2=0.80, RMSE=2.34), while increasing inputs (clay and organic carbon) decreased the accuracy (R2=0.82, RMSE=4.20). Using more inputs, RBFN resulted in better performance in comparison with other methods (R2=0.83, RMSE=2.85). Sensitivity analysis using the connection weight method demonstrated that EC, pH, clay percentage and bulk density are the most important variables in order to explain ESP variability in the region, respectively. Generally, considering the evaluation criteria and the number of used variables of models, ANFIS (with EC and pH as inputs) is the most appropriate method for estimating ESP in Sistan plain.

scholarly journals Application of modern methods: modeling of sedimentary soil ESP content

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Fuzzy Inference ◽  
Evaluation Criteria ◽  
Exchange Capacity ◽  
Fuzzy Inference Systems ◽  
Sodic Soils ◽  
Connection Weight ◽  
Neuro Fuzzy ◽  
Weight Method ◽  
Inference Systems ◽  
Clay Percentage

Knowing the exchangeable sodium percentage (ESP) variations and its values in sodic or saline-sodic soils is essential in order to estimate the amount of soil amendments and better land management. ESP calculated from cation exchange capacity (CEC), and since CEC measurement is difficult and time-consuming, ESP computation is costly and subject to error. Thus, presenting a method to estimate ESP indirectly, by an easily available index is much more efficient and economical. In this study, 296 soil samples collected and analyzed from Sistan plain, southeastern Iran. Soil ESP were predicted by using artificial neural networks (ANNs), comprising radial basis functions (RBFN) and multilayer perceptron (MLP) and adaptive neuro-fuzzy inference systems (ANFIS), and results compared with stepwise linear regression (SLR) method. Results indicated that the SLR models performed poorly in order to estimate ESP (R2 ≤ 0.58 and root mean square error (RMSE ≥ 4.31). Applying fewer inputs (electrical conductivity (EC and pH), ANFIS showed better results (R2=0.80, RMSE=2.34), while increasing inputs (clay and organic carbon) decreased the accuracy (R2=0.82, RMSE=4.20). Using more inputs, RBFN resulted in better performance in comparison with other methods (R2=0.83, RMSE=2.85). Sensitivity analysis using the connection weight method demonstrated that EC, pH, clay percentage and bulk density are the most important variables in order to explain ESP variability in the region, respectively. Generally, considering the evaluation criteria and the number of used variables of models, ANFIS (with EC and pH as inputs) is the most appropriate method for estimating ESP in Sistan plain.

scholarly journals Modified Liu estimators in the linear regression model: An application to Tobacco data

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259991
Author(s):  
Iqra Babar ◽  
Hamdi Ayed ◽  
Sohail Chand ◽  
Muhammad Suhail ◽  
Yousaf Ali Khan ◽  
...  
Keyword(s):  
Linear Regression ◽  
Mean Squared Error ◽  
Prediction Interval ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Vital Role ◽  
Predictor Variables ◽  
Linear Regression Models ◽  
Squared Error ◽  
Optimal Value

Background The problem of multicollinearity in multiple linear regression models arises when the predictor variables are correlated among each other. The variance of the ordinary least squared estimator become unstable in such situation. In order to mitigate the problem of multicollinearity, Liu regression is widely used as a biased method of estimation with shrinkage parameter ‘d’. The optimal value of shrinkage parameter plays a vital role in bias-variance trade-off. Limitation Several estimators are available in literature for the estimation of shrinkage parameter. But the existing estimators do not perform well in terms of smaller mean squared error when the problem of multicollinearity is high or severe. Methodology In this paper, some new estimators for the shrinkage parameter are proposed. The proposed estimators are the class of estimators that are based on quantile of the regression coefficients. The performance of the new estimators is compared with the existing estimators through Monte Carlo simulation. Mean squared error and mean absolute error is considered as evaluation criteria of the estimators. Tobacco dataset is used as an application to illustrate the benefits of the new estimators and support the simulation results. Findings The new estimators outperform the existing estimators in most of the considered scenarios including high and severe cases of multicollinearity. 95% mean prediction interval of all the estimators is also computed for the Tobacco data. The new estimators give the best mean prediction interval among all other estimators. The implications of the findings We recommend the use of new estimators to practitioners when the problem of high to severe multicollinearity exists among the predictor variables.

scholarly journals Estimation of Los Angeles and Micro Deval abrasion values for gneiss and granite using ANFIS approach

2020 ◽  
Vol 40 (1) ◽  
pp. 48-58
Author(s):  
Irina Pachoukova ◽  
Adekunlé A. Salam ◽  
Ayité S. A. Ajavon ◽  
Ampah Kodjo Christophe Johnson
Keyword(s):  
Mechanical Properties ◽  
Los Angeles ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Linear Regression Method ◽  
Linear Regression Models ◽  
Multiple Linear Regression Method ◽  
Validation Tests ◽  
Multiple Linear Regression Models

Gneisses and granites mechanical properties knowledge such as the Los Angeles and Micro-Deval coefficients is very important for the engineer when designing and building civil engineering works. Laboratories test often performed to obtain Los Angeles and Micro-Deval values are expensive and time consuming. For this, the depths (parallel and perpendicular to the foliation planes) obtained during a rock drilling test will be used to estimate its coefficients Los Angeles and Micro-Deval. In this study, an ANFIS approach is used to estimate gneisses and granites Los Angeles and Micro-Deval that have been compared to the Multiple Linear Regression method. For this purpose, we have used a database of a sample size of 80 to determine the parameters of the ANFIS and Multiple Linear Regression models, and a second sample of size 15 used for the validation tests. The results obtained show us that we can estimate the mechanical properties (Los Angeles and Micro-Deval) of gneisses and granites with ANFIS approach.

scholarly journals Spatiotemporal Heterogeneity Analysis of Haemorrhagic Fever with Renal Syndrome in Anqiu City, China, 2000-2014

2019 ◽  
Author(s):  
Fuyan Shi ◽  
Changlan Yu ◽  
Fangyou Li ◽  
Wenfeng Gao ◽  
Liping Yang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Climatic Factors ◽  
Arima Model ◽  
Linear Regression Method ◽  
Climatic Data ◽  
Local Health ◽  
Haemorrhagic Fever ◽  
Linear Regression Models

Abstract Background: The purpose of this study was to explore the dynamics of the occurrence of haemorrhagic fever with renal syndrome (HFRS) and find the potential spatiotemporal factors leading to the incidence of HFRS in Anqiu City. Methods: Monthly reported cases of HFRS and climatic data for 2000–2014 in Anqiu City were obtained. An autoregressive integrated moving average (ARIMA) model was used to fit the HFRS incidence prediction model and predict the epidemic trend in Anqiu City. Multiple linear regression method was used to analyze the temporal relationship between HFRS incidence and meteorological factors during the study period. Results: Spatial analysis results indicated that the annualized average incidence at the town level ranged from 2.18 to 6.09 per 100, 000 among 14 towns, and the western towns in Anqiu City exhibited high endemic levels during the study periods. With high validity, the optimal ARIMA (0, 1, 1) × (0, 1, 1)12 model could be used to predict the HFRS incidence in Anqiu City in 2014. The monthly trend in HFRS incidence was negatively associated with temperature and precipitation and positively associated with average wind speed. Multiple linear regression models showed that precipitation and relative wind speed were key climatic factors contributing to the transmission of HFRS. Conclusions: This study provides evidence that the ARIMA model can be used to fit the fluctuations in HFRS frequency in Anqiu City. Our findings add to the knowledge of the role played by climate factors in HFRS transmission in Anqiu City and can assist local health authorities in the development/refinement of a better strategy to prevent HFRS transmission.

scholarly journals Development and Validation of Predictive Indices for a Continuous Outcome Using Gene Expression Profiles

2010 ◽  
Vol 9 ◽  
pp. CIN.S3805 ◽  
Author(s):  
Yingdong Zhao ◽  
Richard Simon
Keyword(s):  
Gene Expression ◽  
Linear Regression ◽  
Gene Expression Data ◽  
Cross Validation ◽  
Expression Profiles ◽  
Linear Regression Method ◽  
Expression Data ◽  
Linear Regression Models ◽  
Continuous Response ◽  
Real Gene

There have been relatively few publications using linear regression models to predict a continuous response based on microarray expression profiles. Standard linear regression methods are problematic when the number of predictor variables exceeds the number of cases. We have evaluated three linear regression algorithms that can be used for the prediction of a continuous response based on high dimensional gene expression data. The three algorithms are the least angle regression (LAR), the least absolute shrinkage and selection operator (LASSO), and the averaged linear regression method (ALM). All methods are tested using simulations based on a real gene expression dataset and analyses of two sets of real gene expression data and using an unbiased complete cross validation approach. Our results show that the LASSO algorithm often provides a model with somewhat lower prediction error than the LAR method, but both of them perform more efficiently than the ALM predictor. We have developed a plug-in for BRB-ArrayTools that implements the LAR and the LASSO algorithms with complete cross-validation.

scholarly journals A multiple linear regression GIS module using spatial variables to model orographic rainfall

2004 ◽  
Vol 6 (1) ◽  
pp. 39-56 ◽  
Author(s):  
S. Naoum ◽  
I. K. Tsanis
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Spatial Interpolation ◽  
Three Dimensional ◽  
Predictor Variables ◽  
Test Case ◽  
Linear Regression Method ◽  
Linear Regression Models ◽  
Orographic Rainfall ◽  
Interpolation Techniques

This paper aims to document the development of a new GIS-based spatial interpolation module that adopts a multiple linear regression technique. The functionality of the GIS module is illustrated through a test case represented by the island of Crete, Greece, where the models generated were applied to locations where estimates of annual precipitation were required. The response variable is ‘precipitation’ and the predictor variables are ‘elevation’, ‘longitude’ and ‘latitude’, or any combination of these. The module is capable of performing a sequence of tasks which will eventually lead to an estimation of mean areal precipitation and the total volume of precipitation. In addition, it can generate up to nine predictor variables and their parameters, and can estimate areal rainfall for a user-specified three-dimensional extent. The developed module performed satisfactorily. Precipitation estimates at ungauged locations were obtained using the multiple linear regression method in addition to some conventional spatial interpolation techniques (i.e. IDW, Spline, Kriging, etc.). The multiple linear regression models provided better estimates than the other spatial interpolation techniques.

scholarly journals Stiffness and Strength of Stabilized Organic Soils—Part II/II: Parametric Analysis and Modeling with Machine Learning

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 218
Author(s):  
Negin Yousefpour ◽  
Zenon Medina-Cetina ◽  
Francisco G. Hernandez-Martinez ◽  
Abir Al-Tabbaa
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Mechanical Behavior ◽  
Soil Stabilization ◽  
Organic Soils ◽  
Linear Regression Method ◽  
Compression Tests ◽  
Linear Regression Models ◽  
Soil Mixing ◽  
Ann Models

Predicting the range of achievable strength and stiffness from stabilized soil mixtures is critical for engineering design and construction, especially for organic soils, which are often considered “unsuitable” due to their high compressibility and the lack of knowledge about their mechanical behavior after stabilization. This study investigates the mechanical behavior of stabilized organic soils using machine learning (ML) methods. ML algorithms were developed and trained using a database from a comprehensive experimental study (see Part I), including more than one thousand unconfined compression tests on organic clay samples stabilized by wet soil mixing (WSM) technique. Three different ML methods were adopted and compared, including two artificial neural networks (ANN) and a linear regression method. ANN models proved reliable in the prediction of the stiffness and strength of stabilized organic soils, significantly outperforming linear regression models. Binder type, mixing ratio, soil organic and water content, sample size, aging, temperature, relative humidity, and carbonation were the control variables (input parameters) incorporated into the ML models. The impacts of these factors were evaluated through rigorous ANN-based parametric analyses. Additionally, the nonlinear relations of stiffness and strength with these parameters were developed, and their optimum ranges were identified through the ANN models. Overall, the robust ML approach presented in this paper can significantly improve the mixture design for organic soil stabilization and minimize the experimental cost for implementing WSM in engineering projects.

Sign in / Sign up

Export Citation Format

Share Document