Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system

Tutmez B.

NEURAL COMPUTING & APPLICATIONS, vol.19, no.3, pp.499-505, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 19 Issue: 3
  • Publication Date: 2010
  • Doi Number: 10.1007/s00521-009-0326-3
  • Journal Name: NEURAL COMPUTING & APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.499-505
  • Inonu University Affiliated: Yes

Abstract

Aquifer porosity indicates the storage groundwater capacity and groundwater quality. It may be measured via different techniques. This paper presents a novel spatial methodology based on radial basis function (RBF) and neuro-fuzzy inference system for modelling the porosity. Use of the point cumulative semimadogram in RBF as a spatial measure is a novel contribution. In addition, the methodology examines the use of a neural network-based fuzzy inference system for porosity estimation. Performance comparisons with conventional methods show that the proposed spatial model has high modelling and generalization capability.