scholarly journals Correcting field determination of elemental contents in soils via portable X-ray fluorescence spectrometry

2020 ◽  
Vol 44 ◽  
Author(s):  
Thaís Santos Branco Dijair ◽  
Fernanda Magno Silva ◽  
Anita Fernanda dos Santos Teixeira ◽  
Sérgio Henrique Godinho Silva ◽  
Luiz Roberto Guimarães Guilherme ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Prediction Models ◽  
Elemental Content ◽  
Auxiliary Variables ◽  
Linear Regression Models ◽  
X Ray ◽  
Laboratory Conditions ◽  
Elemental Contents ◽  
Field And Laboratory Conditions

ABSTRACT Portable X-ray fluorescence (pXRF) spectrometry has been useful worldwide for determining soil elemental content under both field and laboratory conditions. However, the field results are influenced by several factors, including soil moisture (M), soil texture (T) and soil organic matter (SOM). Thus, the objective of this work was to create linear mathematical models for conversion of Al2O3, CaO, Fe, K2O, SiO2, V, Ti and Zr contents obtained by pXRF directly in field to those obtained under laboratory conditions, i.e., in air-dried fine earth (ADFE), using M, T and SOM as auxiliary variables, since they influence pXRF results. pXRF analyses in field were performed on 12 soil profiles with different parent materials. From them, 59 samples were collected and also analyzed in the laboratory in ADFE. pXRF field data were used alone or combined to M, T and SOM data as auxiliary variables to create linear regression models to predict pXRF ADFE results. The models accuracy was assessed by the leave-one-out cross-validation method. Except for light-weight elements, field results underestimated the total elemental contents compared with ADFE. Prediction models including T presented higher accuracy to predict Al2O3, SiO2, V, Ti and Zr, while the prediction of Fe and K2O contents was insensitive to the addition of the auxiliary variables. The relative improvement (RI) in the prediction models were greater in predictions of SiO2 (T+SOM: RI=22.29%), V (M+T: RI=18.90%) and Ti (T+SOM: RI=11.18%). This study demonstrates it is possible to correct field pXRF data through linear regression models.

Comparison of Linear Regression Models for Quantitative Geochemical Analysis: An Example Using X-Ray Fluorescence Spectrometry

2005 ◽  
Vol 29 (3) ◽  
pp. 271-284 ◽  
Author(s):  
Mirna Guevara ◽  
Surendra P. Verma ◽  
Fernando Velasco-Tapia ◽  
Rufino Lozano-Santa Cruz ◽  
Patricia Girón
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Fluorescence Spectrometry ◽  
Geochemical Analysis ◽  
Linear Regression Models ◽  
X Ray

scholarly journals Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

2017 ◽  
Vol 44 (12) ◽  
pp. 994-1004 ◽  
Author(s):  
Ivica Androjić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Linear Regression Models ◽  
Artificial Neural ◽  
Multiple Linear Regression Models

The oscillation of asphalt mix composition on a daily basis significantly affects the achieved properties of the asphalt during production, thus resulting in conducting expensive laboratory tests to determine existing properties and predicting the future results. To decrease the amount of such tests, a development of artificial neural network and multiple linear regression models in the prediction process of predetermined dependent variables air void and soluble binder content is presented. The input data were obtained from a single laboratory and consists of testing 386 mixes of hot mix asphalt (HMA). It was found that it is possible and desirable to apply such models in the prediction process of the HMA properties. The final aim of the research was to compare results of the prediction models on an independent dataset and analyze them through the boundary conditions of technical regulations and the standard EN 13108-21.

scholarly journals Generation of the bathymetry of a eutrophic shallow lake using WorldView-2 imagery

2013 ◽  
Vol 16 (1) ◽  
pp. 50-59 ◽  
Author(s):  
Onur Yuzugullu ◽  
Aysegul Aksoy
Keyword(s):  
Linear Regression ◽  
Shallow Lake ◽  
Regression Models ◽  
Prediction Models ◽  
Satellite Image ◽  
Linear Regression Models ◽  
Remotely Sensed Data ◽  
Non Linear ◽  
Eutrophic Shallow Lake ◽  
Bathymetric Map

In this study, water depth distribution (bathymetric map) in a eutrophic shallow lake was determined using a WorldView-2 multispectral satellite image. Lake Eymir in Ankara (Turkey) was the study site. In order to generate the bathymetric map of the lake, image and data processing, and modelling were applied. First, the bands that would be used in depth prediction models were determined through statistical and multicollinearity analyses. Then, data screening was performed based on the standard deviation of standardized residuals (SD_SR) of depth values determined through preliminary linear regression models. This analysis indicated the sampling points utilized in depth modelling. Finally, linear and non-linear regression models were developed to predict the depths in Lake Eymir based on remotely sensed data. The non-linear regression model performed slightly better compared to the linear one in predicting the depths in Lake Eymir. Coefficients of determination (R2) up to 0.90 were achieved. In general, the bathymetric map was in agreement with observations except at re-suspension areas. Yet, regression models were successful in defining the shallow depths at shore, as well as at the inlet and outlet of the lake. Moreover, deeper locations were successfully identified.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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