Preprints
https://doi.org/10.5194/hess-2022-86
https://doi.org/10.5194/hess-2022-86
01 Jun 2022
 | 01 Jun 2022
Status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Prediction of groundwater quality index to assess suitability for drinking purpose using averaged neural network and geospatial analysis

Seok Hyun Ahn, Do Hwan Jeong, MoonSu Kim, Tae Kwon Lee, and Hyun-Koo Kim

Abstract. The aims of this study were to determine the groundwater quality index (GQI) using an averaged neural network and evaluate its field applicability with two-dimensional (2D) spatial analysis. The GQI was computed using 29 water quality parameters obtained at 3,552 portable groundwater wells used as drinking water sources. The GQI was divided into the following three grades: ‘worrisome’, <0.89 (20.1 % of the wells); ‘good’, 0.89–0.94 (62.8 %); and ‘very good’, >0.94 (17.1 %). Based on the random forest, the most important water quality parameters were general bacteria, turbidity and nitrate. The 2D spatial analysis confirmed notable differences in the GQI grades among regions. The 10-year long-term groundwater quality monitoring in the ‘worrisome’ grade showed the nitrate and chloride concentrations have continuously increased. These results indicate that the coupling of the GQI with 2D spatial analysis is a promising approach that can be applied in groundwater management and vulnerability assessment.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Seok Hyun Ahn, Do Hwan Jeong, MoonSu Kim, Tae Kwon Lee, and Hyun-Koo Kim

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-86', Anonymous Referee #1, 17 Jun 2022
    • AC1: 'Reply on RC1', Tae Kwon Lee, 21 Nov 2022
  • RC2: 'Comment on hess-2022-86', Anonymous Referee #2, 10 Nov 2022
    • AC2: 'Reply on RC2', Tae Kwon Lee, 21 Nov 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-86', Anonymous Referee #1, 17 Jun 2022
    • AC1: 'Reply on RC1', Tae Kwon Lee, 21 Nov 2022
  • RC2: 'Comment on hess-2022-86', Anonymous Referee #2, 10 Nov 2022
    • AC2: 'Reply on RC2', Tae Kwon Lee, 21 Nov 2022
Seok Hyun Ahn, Do Hwan Jeong, MoonSu Kim, Tae Kwon Lee, and Hyun-Koo Kim
Seok Hyun Ahn, Do Hwan Jeong, MoonSu Kim, Tae Kwon Lee, and Hyun-Koo Kim

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Short summary
We collected water quality datasets including 29 water quality parameters and 3,552 wells of groundwater for drinking. A simple water quality index with averaged neural network model and geospatial analysis are sufficient to select priority groundwater quality management areas in South Korea. We believe that our study makes a significant contribution to the water resource management.