relation: https://khub.utp.edu.my/scholars/7554/
title: Comparison of support vector machines kernel functions for pore-water pressure modeling
creator: Babangida, N.M.
creator: Yusof, K.W.
creator: Mustafa, M.R.
creator: Isa, M.H.
description: Modeling pore-water pressure (PWP) responses to rainfall is an important part of monitoring hydrological behavior of hill slope. Of recent, soft computing techniques had been used to model these responses. Using support vector regression (SVR) these responses can be modeled with very good accuracy. However, selection of appropriate kernel for such modeling is a necessity. Using PWP and rainfall data from an instrumented slope, four kernel function (linear, sigmoid, polynomial and radial basis function) were used to develop four Models to predict PWP. Input features were selected using a wrapper algorithm, and the SVR meta-parameters were calibrated using k-fold cross validation and grid search. The radial basis function (RBF) was found to be the most suitable for modeling PWP responses, due to its competitive results and less complexity in implementation. © 2016 Taylor & Francis Group, London.
publisher: CRC Press/Balkema
date: 2016
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Babangida, N.M. and Yusof, K.W. and Mustafa, M.R. and Isa, M.H.  (2016) Comparison of support vector machines kernel functions for pore-water pressure modeling.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009801596&doi=10.1201%2fb21942-58&partnerID=40&md5=c5eca8de16bcb3975cf0687d3e618e74
relation: 10.1201/b21942-58
identifier: 10.1201/b21942-58