Groundwater productivity potential mapping using frequency ratio and evidential belief function and artificial neural network models: Focus on topographic factors

Jeong Cheol Kim, Hyung Sup Jung, Saro Lee

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

This study analysed groundwater productivity potential (GPP) using three different models in a geographic information system (GIS) for Okcheon city, Korea. Specifically, we have used variety topography factors in this study. The models were based on relationships between groundwater productivity (for specific capacity (SPC) and transmissivity (T)) and hydrogeological factors. Topography, geology, lineament, land-use and soil data were first collected, processed and entered into the spatial database. T and SPC data were collected from 86 well locations. The resulting GPP map has been validated in under the curve analysis area using well data not used for model training. The GPP maps using artificial neural network (ANN), frequency ratio (FR) and evidential belief function (EBF) models for T had accuracies of 82.19%, 81.15% and 80.40%, respectively. Similarly, the ANN, FR and EBF models for SPC had accuracies of 81.67%, 81.36% and 79.89%, respectively. The results illustrate that ANN models can be useful for the development of groundwater resources.

Original languageEnglish
Pages (from-to)1436-1451
Number of pages16
JournalJournal of Hydroinformatics
Volume20
Issue number6
DOIs
StatePublished - Nov 2018

Keywords

  • Artificial neural network
  • Frequency ratio
  • GIS
  • Groundwater productivity potential
  • South Korea

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