Delineation of groundwater quality locations suitable for target end-use purposes through deep neural network models.

Groundwater is the main source of water for beverages, and its quality varies depending on extraction location; this is particularly the case in regions with complex geology, topography, and multiple forms of land use. Thus, it is important to determine a suitable groundwater extraction location based on intended water use and the related water quality standards. In this study, deep neural network (DNN) models and GIS data relating to the groundwater quality were applied to estimate potential maps of Gangwon province, where groundwater is frequently extracted for drinking purpose in South Korea. These maps specify areas wherein the groundwater quality is conducive for being used as mineral water and water for brewing coffee (hereafter referred as 'coffee water'). Sensitivity analysis identified how inputs were sensitive to model estimation, and showed that land-use variables were the most sensitive. The importance of each variable quantified how good or bad its region is for the desired groundwater. The overall features of importance were similar between mineral water and coffee water. However, with differences in hydrogeological units, carbonate rock was a variable of high positive importance for mineral water, while metamorphic rock was its equivalent for coffee water. Our results offer a potential map of desired groundwater quality in the absence of a detailed understanding of the underlying hydrochemical processes governing groundwater quality. Additionally, the development of such a potential mapping model can lead people to determine the appropriate development area of groundwater for their respective purposes. This article is protected by copyright. All rights reserved.