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Delineation of groundwater quality locations suitable for target end‐use purposes through deep neural network models
Journal of Environmental Quality ( IF 2.2 ) Pub Date : 2021-02-12 , DOI: 10.1002/jeq2.20206
Sanghoon Lee 1 , Dugin Kaown 1 , Eun‐Hee Koh 1 , Kyung‐Seok Ko 2 , Kang‐Kun Lee 1
Affiliation  

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 groundwater quality were applied to estimate potential maps of Gangwon Province in South Korea, where groundwater is frequently extracted for drinking purposes. These maps specify areas where 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; 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 help to determine the appropriate development area of groundwater for their respective purposes.

中文翻译:

通过深度神经网络模型描述适合目标最终用途的地下水水质位置

地下水是饮料水的主要来源,其质量随提取位置的不同而不同。在地质,地形复杂,土地使用多种形式的地区尤其如此。因此,重要的是要根据预期的用水量和相关的水质标准确定合适的地下水抽取位置。在这项研究中,与地下水质量有关的深度神经网络(DNN)模型和GIS数据被用于估算韩国江原道的潜在地图,该国经常抽取地下水用于饮用。这些地图指定了地下水质量有利于用作矿泉水和用于冲泡咖啡的水(以下称为“咖啡水”)的区域。敏感性分析确定了输入对模型估计的敏感性,并表明土地利用变量最敏感。每个变量的重要性量化了其区域对于所需地下水的好坏程度。重要性的总体特征在矿泉水和咖啡水之间相似。然而,由于水文地质单位的差异,碳酸盐岩对矿泉水具有高度积极的意义。变质岩相当于咖啡水。我们的结果提供了潜在地下水质量的潜在地图,而对控制地下水质量的基本水化学过程缺乏详细的了解。另外,开发这种潜在的测绘模型可以帮助确定用于其各自目的的适当的地下水开发区域。
更新日期:2021-03-31
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