Sungwoo Nam1·Eungyu Park2*·Myeong-jae Yi1·Seonkeum Jeon1·Hyemin Jung1·Jeongwoo Kim1
1Geogreen21 Co, Ltd, Seoul, Korea
2Department of Geology, Kyungpook National University, Daegu, Korea
This article is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Groundwater is used in many areas in food industry such as food manufacturing, food processing, cooking, and liquor industry etc. in Korea. As groundwater occupies a large portion of food industry, it is necessary to predict deterioration of water quality to ensure the safety of food water since using undrinkable groundwater has a ripple effect that can cause great harm or anxiety to food users. In this study, spatiotemporal data aggregation method was used in order to obtain spatially representative data, which enable prediction of groundwater quality change in a small watershed. In addition, a highly reliable predictive model was developed to estimate long-term changes in groundwater quality by applying a non-parametric segmented regression technique. Two pilot watersheds were selected where a large number of companies use groundwater for food water, and the appropriateness of the model was assessed by comparing the model-produced values with those obtained by actual measurements. The result of this study can contribute to establishing a customized food water management system utilizing big data that respond quickly, accurately, and pre-emptively to changes in groundwater quality and pollution. It is also expected to contribute to the improvement of food safety management.
Keywords: food processing water, groundwater, prediction model, segmented regression, data aggregation
2021; 26(6): 165-175
Published on Dec 31, 2021
Department of Geology, Kyungpook National University, Daegu, Korea