Widespread climate change can impact on groundwater conditions. We evaluated the groundwater data for 1996-2008 obtained from the National Groundwater Monitoring Stations (NGMS), in the context of the global warming effect. Indications of air temperature and sea level rises were evident in the period. The groundwater levels were generally decreasing and most of this decrease was attributed to increased pumping nationwide, but no indication for any effect of sea level rise on groundwater level in coastal aquifers was found. The electrical conductivity (EC) values of groundwaters were very high in metropolitan and industrial areas, which were indicative of anthropogenic groundwater contamination and progressive groundwater quality deterioration. A systematic EC rise in coastal groundwaters, as a possible result of the sea level rise, was not observed. The groundwater temperature variation was the most striking. The majority of the monitored shallow and deep groundwaters exhibited increasing trends at mean rates of 0.04-0.09°C/yr. The widespread and prevailing increase in groundwater temperature nationwide, with increasing air temperature, is strongly indicative of the effect of global warming. The increasing trend became more distinctive every year. However, these significant conclusions require further groundwater monitoring and re-evaluation.

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The availability of groundwater is of concern. The demand for groundwater in Korea increased by more than 100% during the period 1994–2014. This problem will increase with population growth. Thus, a reliable groundwater analysis model for regional scale studies is needed. This study used the geographical information system (GIS) data and machine learning to map groundwater potential in Gangneung-si, South Korea. A spatial correlation performed using the frequency ratio was applied to determine the relationships between groundwater productivity (transmissivity data from 285 wells) and various factors. This study used four topography factors, four hydrological factors, and three geological factors, along with the normalized difference wetness index and land use and soil type. Support vector regression (SVR) and metaheuristic optimization algorithms—namely, grey wolf optimization (GWO), and particle swarm optimization (PSO), were used in the construction of the groundwater potential map. Model validation based on the area under the receiver operating curve (AUC) was used to determine model accuracy. The AUC values of groundwater potential maps made using the SVR, SVR_GWO, and SVR_PSO algorithms were 0.803, 0.878, and 0.814, respectively. Thus, the application of optimization algorithms increased model accuracy compared to the standard SVR algorithm. The findings of this study improve our understanding of groundwater potential in a given area and could be useful for policymakers aiming to manage water resources in the future.

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