Using the Support Vector Regression Approach to Model Human Performance

2011 ◽  
Vol 41 (3) ◽  
pp. 410-417 ◽  
Author(s):  
Luzheng Bi ◽  
Omer Tsimhoni ◽  
Yili Liu
Keyword(s):  
Support Vector Regression ◽  
Human Performance ◽  
Support Vector ◽  
Regression Approach

A support vector regression approach to predict geotechnical properties of soils from electrical spectra based on Jonscher parameterization

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. EN39-EN48
Author(s):  
Fred Kofi Boadu
Keyword(s):  
Support Vector Regression ◽  
Geophysical Methods ◽  
Geotechnical Properties ◽  
Unit Weight ◽  
Support Vector ◽  
Natural Soil ◽  
Regression Approach ◽  
Model Training ◽  
Engineering Geophysics ◽  
Conductivity Spectra

Electrical-conductivity spectra of soils contain valuable information about their texture, structure, and composition that can be linked to their geotechnical properties. Concurrent measurements of electrical spectra in the frequency range of 0.01 Hz to 10 kHz and geotechnical properties, that is, the dry unit weight [Formula: see text], modulus of elasticity [Formula: see text], and the hydraulic conductivity [Formula: see text], are performed on natural soil samples in a laboratory environment. The electrical spectra are modeled with the Jonscher fractal power law model characterized by three parameters: DC conductivity [Formula: see text], transition frequency [Formula: see text], and an exponent [Formula: see text]. We explore a machine-learning technique, the support vector regression (SVR) methodology, to model and predict the geotechnical properties from the Jonscher parameters, and we compare our results with the predictions of multiple linear regression (MLR). For model training and testing, the Jonscher parameters are used as the input, and a geotechnical parameter is used as the output. Model comparisons indicate that the developed SVR models predict [Formula: see text] with an [Formula: see text], predict [Formula: see text] with [Formula: see text], and predict [Formula: see text] with [Formula: see text]. In comparison, MLR models predict [Formula: see text] with an [Formula: see text], [Formula: see text] with [Formula: see text], and [Formula: see text] with [Formula: see text]. The results illustrate that the SVR models are more accurate, reliable, and achieve better performance for predicting the geotechnical properties from the electrical parameters in comparison to the predictions of the MLR models. Our study offers an opportunity in our quest in using noninvasive electrical geophysical methods to obtain geotechnical properties of soils, and it has broad implications in engineering geophysics.

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