Spatial mapping of the groundwater potential of the Geum River basin using ensemble models based on remote sensing images

Jeong Cheol Kim, Hyung Sup Jung, Saro Lee

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

This study analyzed the Groundwater Productivity Potential (GPP) of Okcheon city, Korea, using three different models. Two of these three models are data mining models: Boosted Regression Tree (BRT) model and Random Forest (RF) model. The other model is the Logistic Regression (LR) model. The three models are based on the relationship between groundwater-productivity data (specific capacity (SPC) and transmissivity (T)) and the related hydro-geological factors from thematic maps, such as topography, lineament, geology, land cover, and etc. The thematic maps which are generated from the remote sensing images. Groundwater productivity data were collected from 86 wells locations. The resulting GPP maps were validated through area-under-the-curve (AUC) analysis using wells data that had not been used for training the model. When T was used in the BRT, RF, and LR models, the obtained GPP maps had 81.66%, 80.21%, and 85.04% accuracy, respectively, and when SPC was used, the maps had 81.53%, 78.57%, and 82.22% accuracy, respectively. The LR model, which is a statistical model, showed the highest verification accuracy, also the other two models showed high accuracies. These observations indicate that all three models can be useful for groundwater resource development.

Original languageEnglish
Article number2285
JournalRemote Sensing
Volume11
Issue number19
DOIs
StatePublished - 1 Oct 2019

Keywords

  • Boosted Regression Tree
  • GIS
  • Groundwater
  • Logistic regression
  • Random forest
  • Remote sensing

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