Determination of Electrical Resistivity of Soil Based on Thermal Resistivity Using RVM and MPMR

Authors

  • Pijush Samui
  • Dookie Kim
https://doi.org/10.3311/PPci.8206

Abstract

This article adopts Relevance Vector Machine (RVM) and Minimax Probability Machine Regression (MPMR) for prediction Soil Electrical Resistivity(RE) of soil. RVM uses an improper hierarchical prior. It optimizes over hyperparameters. MPMR is a probabilistic model. Two models (MODEL I and MODEL II) have been adopted. Percentage sum of the gravel and sand size fractions (F) and Soil Thermal Resistivity(RT) has been takes as inputs in MODEL I. MODEL II uses F,RT and saturation of soils(S) as input variables. The results of RVM and MPMR have  been compared with the Artificial Neural Network (ANN). The developed RVM and MPMR proves his ability for prediction of RE of soil.

Keywords:

Soil Electrical Resistivity, Soil Thermal Resistivity, Relevance Vector Machine, Minimax Probability Machine Regression, Artificial Neural Network

Published Online

2016-08-31

How to Cite

Samui, P., Kim, D. Determination of Electrical Resistivity of Soil Based on Thermal Resistivity Using RVM, MPMR, Periodica Polytechnica Civil Engineering, 60(4), pp. 511–515, 2016. https://doi.org/10.3311/PPci.8206

Issue

Section

Research Article