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An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping
Open AccessArticle

Spatial Mapping of the Groundwater Potential of the Geum River Basin Using Ensemble Models Based on Remote Sensing Images

1
National Institute of Ecology (NIE), 1210 Geumgang-ro, Maseo-myeon, Seocheon-gun, Chungcheongnam-do 33657, Korea
2
Department of Geoinformatics, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul 02504, Korea
3
Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea
4
Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 305-350, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2285; https://doi.org/10.3390/rs11192285
Received: 3 September 2019 / Revised: 23 September 2019 / Accepted: 26 September 2019 / Published: 30 September 2019
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. View Full-Text
Keywords: groundwater; remote sensing; GIS; random forest; Boosted Regression Tree; logistic regression groundwater; remote sensing; GIS; random forest; Boosted Regression Tree; logistic regression
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MDPI and ACS Style

Kim, J.-C.; Jung, H.-S.; Lee, S. Spatial Mapping of the Groundwater Potential of the Geum River Basin Using Ensemble Models Based on Remote Sensing Images. Remote Sens. 2019, 11, 2285.

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