01 Jun 2022
01 Jun 2022
Status: this preprint is currently under review for the journal HESS.

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

Seok Hyun Ahn1,, Do Hwan Jeong2,, MoonSu Kim2, Tae Kwon Lee1, and Hyun-Koo Kim2 Seok Hyun Ahn et al.
  • 1Department of Environmental Engineering, Yonsei University, Wonju 26493, South Korea
  • 2Soil and Groundwater Division, National Institute of Environmental Research, Incheon 22689, South Korea
  • These authors contributed equally to this work.

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.

Seok Hyun Ahn et al.

Status: open (until 08 Oct 2022)

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 reply

Seok Hyun Ahn et al.

Seok Hyun Ahn et al.


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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.