Abstract
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.
Highlights
Water resources are an essential aspect for living in this world, including surface water and groundwater, and are recycled through evaporation, precipitation and surface runoff.Recent climate change projections point to increased spatial and temporal heterogeneity in the water cycle, which would lead to water demand outstripping supply [1]
This study developed a machine learning approach for analyzing Remote sensing (RS) and geographical information system (GIS) data to map groundwater potential; combining statistical models and machine learning algorithms (FR and Support vector regression (SVR)) can facilitate scientific decision-making as it pertains to a variety of problems [85]
This study focuses on the application of machine learning methods and integration with remote sensing for groundwater potential mapping
Summary
Water resources are an essential aspect for living in this world, including surface water and groundwater, and are recycled through evaporation, precipitation and surface runoff. Recent climate change projections point to increased spatial and temporal heterogeneity in the water cycle, which would lead to water demand outstripping supply [1]. Over the several decades, demand for water resources, including groundwater, is expected to increase significantly [2,3,4]. Approximately 20% of water consumed by humans is derived from groundwater, and this proportion is projected to increase over the several decades [6,7]. Climatic conditions have an important role in groundwater availability, affecting both spill patterns and runoff time [2].
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