Spatial Prediction of Soil Salinity Using Electromagnetic Induction Techniques: 1. Statistical Prediction Models: A Comparison of Multiple Linear Regression and Cokriging

1995 ◽  
Vol 31 (2) ◽  
pp. 373-386 ◽  
Author(s):  
Scott M. Lesch ◽  
David J. Strauss ◽  
James D. Rhoades
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Soil Salinity ◽  
Electromagnetic Induction ◽  
Prediction Models ◽  
Spatial Prediction ◽  
Statistical Prediction

scholarly journals Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

2016 ◽  
Vol 16 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Samander Ali Malik ◽  
Assad Farooq ◽  
Thomas Gereke ◽  
Chokri Cherif
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Tensile Properties ◽  
Regression Models ◽  
Prediction Models ◽  
Research Work ◽  
Absolute Error ◽  
Knowledge Domain ◽  
Artificial Neural

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

scholarly journals Bruise susceptibilities of kiwifruit as affected by impact and fruit properties

2012 ◽  
Vol 58 (No. 3) ◽  
pp. 107-113 ◽  
Author(s):  
E. Ahmadi
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Fruit Quality ◽  
Prediction Models ◽  
Radius Of Curvature ◽  
Regression Analyses ◽  
Probability Level ◽  
Absorbed Energy ◽  
Bruise Susceptibility ◽  
Damage Susceptibility

Kiwifruit bruise damage is a common postharvest disorder that substantially reduces fruit quality and marketability. Fruit bruise cause tissue softening and make them more susceptible to undesired agents such as diseases-inducing agents. Factors that affect kiwifruit bruise susceptibility such as impact properties and fruit properties were investigated. Two bruise prediction models were constructed for the damage susceptibility of kiwifruit (measured by absorbed energy) using multiple linear regression analyses. Kiwifruits were subjected to dynamic loading by means of a pendulum at three levels of impact. Significant effects of acoustic stiffness, temperature and the radius of curvature and some interactions on bruising were obtained at 5% probability level.

On the Multiple Linear Regression and Artificial Neural Networks for Strength Prediction of Soil-Based Controlled Low-Strength Material

2014 ◽  
Vol 597 ◽  
pp. 349-352 ◽  
Author(s):  
Li Jeng Huang ◽  
Yeong Nain Sheen ◽  
Duc Hien Le
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Prediction Models ◽  
Laboratory Data ◽  
Pulse Velocity ◽  
Strength Prediction ◽  
Strength Material ◽  
Artificial Neural ◽  
Low Strength ◽  
Controlled Low Strength Material

This paper presents two approaches, multiple linear regression (MLR) and artificial neural network (ANN), to develop predictive models for unconfined compressive strength of soil-based controlled low-strength material (CLSM). Our obtained laboratory data conducting on the soil-based CLSM were employed for analysis. Two strength prediction models were proposed: (1) strength is assumed to be a function of mix proportion and curing period; and (2) it is estimated from measured ultrasonic pulse velocity combined with effect of mixture parameters and curing ages. In each model, three predicted formulas were developed; one from MLR and two from ANN. It was showed that all the proposed equations have a well-predicted capacity.

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 SPATIAL PREDICTION OF SOIL SALINITY USING ELECTROMAGNETIC INDUCTION TECHNIQUES

2015 ◽  
Vol 6 (3) ◽  
pp. 403-413
Author(s):  
M. Abdel-Hamid ◽  
Y. Nasr ◽  
M. Ismail ◽  
R. Yacoub ◽  
A. Elwan
Keyword(s):  
Soil Salinity ◽  
Electromagnetic Induction ◽  
Spatial Prediction
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