scholarly journals Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model

2017 ◽  
Vol 184 ◽  
pp. 149-175 ◽  
Author(s):  
Ravinesh C Deo ◽  
Ozgur Kisi ◽  
Vijay P Singh
Keyword(s):  
Support Vector Machine ◽  
Least Square ◽  
Multivariate Adaptive Regression Spline ◽  
Support Vector ◽  
Drought Forecasting ◽  
Regression Spline ◽  
Eastern Australia ◽  
Adaptive Regression

Multivariate Adaptive Regression Spline and Least Square Support Vector Machine for Prediction of Undrained Shear Strength of Clay

2012 ◽  
Vol 3 (2) ◽  
pp. 33-42 ◽  
Author(s):  
Pijush Samui ◽  
Pradeep Kurup
Keyword(s):  
Support Vector Machine ◽  
Shear Strength ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Undrained Shear Strength ◽  
Least Square ◽  
Support Vector ◽  
Regression Spline ◽  
Adaptive Regression ◽  
Undrained Shear

This study adopts Multivariate Adaptive Regression Spline (MARS) and Least Square Support Vector Machine (LSSVM) for prediction of undrained shear strength (su) of clay, based Cone Penetration Test (CPT) data. Corrected cone resistance (qt), vertical total stress (sv), hydrostatic pore pressure (u0), pore water pressure at the cone tip (u1), and pore water pressure just above the cone base (u2) are used as input parameters for building the MARS and LSSVM models. The developed MARS and LSSVM models give simple equations for prediction of su. A comparative study between MARS and LSSSM is presented. The results confirm that the developed MARS and LSSVM models are robust for prediction of su.

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