Abstract
For the sustainable use of groundwater, this study analyzed groundwater productivity-potential using a decision-tree approach in a geographic information system (GIS) in Boryeong and Pohang cities, Korea. The model was based on the relationship between groundwater-productivity data, including specific capacity (SPC), and its related hydrogeological factors. SPC data which is measured and calculated for groundwater productivity and data about related factors, including topography, lineament, geology, forest and soil data, were collected and input into a spatial database. A decision-tree model was applied and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The resulting groundwater-productivity-potential (GPP) maps were validated using area-under-the-curve (AUC) analysis with the well data that had not been used for training the model. In the Boryeong city, the CHAID and QUEST algorithms had accuracies of 83.31% and 79.47%, and in the Pohang city, the CHAID and QUEST algorithms had accuracies of 86.18% and 80.00%. As another validation, the GPP maps were validated by comparing the actual SPC data. As the result, in the Boryeong city, the CHAID and QUEST algorithms had accuracies of 96.55% and 94.92% and in the Pohang city, the CHAID and QUEST algorithms had accuracies of 87.88% and 87.50%. These results indicate that decision-tree models can be useful for development of groundwater resources.
Highlights
Groundwater demand has increased because groundwater is one of the most important natural resources, supporting human health, economic development, and ecological diversity
chi-squared automatic interaction detector (CHAID) and QUEST algorithms had accuracies of 87.88% and 87.50%. These results indicate that decision-tree models can be useful for development of groundwater resources
The probability in the leaf node was considered as the groundwater productivity potential index (GPPI)
Summary
Groundwater demand has increased because groundwater is one of the most important natural resources, supporting human health, economic development, and ecological diversity. It is readily obtainable anywhere, provides excellent water quality, and has low development costs [1]. To ensure that a secure amount of water is available, systematic development and management planning should be established. Considering that the amount of groundwater used in Korea increased by more than 210% [3] between 1994 and 2008, development and utilization at the national level currently do not meet people’s growing needs. The development of reliable analytical methods and models for predicting locations that have GPP is urgently needed for systematic development, efficient management, and sustainable use of groundwater resources
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