ASSESSMENT OF LIQUEFACTION POTENTIAL OF SOIL USING MULTI-LINEAR REGRESSION MODELING

Our study implements a logistic regression within a Bayesian measurement error framework to incorporate uncertainty in predictor variables and allow for a probabilistic

Earth Planets Space, 58, 331?341, 2006 Multi step prediction of Dst index using singular spectrum analysis and locally linear neurofuzzy modeling Javad Sharifi, Babak N Araabi, and Caro

The proposed system will integrate the data obtained from repository, weather department and by applying machine learning algorithm: Multiple Linear Regression, a

Artificial neural network (ANN), Bayesian classifier and Multiple linear regression (MLR) were used to generate the prediction models using a dataset of 699 patients..

When for any reason, the soils’ properties at a greater depth could not be determined in the laboratory, the linear regression equation can be used to predict

By incorporating a multi-linear regression approach, we have been able to establish significant relationships to predict deep drainage beneath annual and, tree and perennial

The multiple linear regression (MLR) was used to build the linear quantitative structure-property relationship (QSPR) model for the prediction of the molar diamagnetic

A multi-level fuzzy linear regression model was designed to take industry energy demand in the last year, value added of industry sector, number of industries and investment