Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South Korea
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 geograp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/6/1196 |
| _version_ | 1797540534116417536 |
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| author | Muhammad Fulki Fadhillah Saro Lee Chang-Wook Lee Yu-Chul Park |
| author_facet | Muhammad Fulki Fadhillah Saro Lee Chang-Wook Lee Yu-Chul Park |
| author_sort | Muhammad Fulki Fadhillah |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:02:33Z |
| format | Article |
| id | doaj.art-0b4196b26bd445aa831fcc299ef28df1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T13:02:33Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0b4196b26bd445aa831fcc299ef28df12023-11-21T11:23:01ZengMDPI AGRemote Sensing2072-42922021-03-01136119610.3390/rs13061196Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South KoreaMuhammad Fulki Fadhillah0Saro Lee1Chang-Wook Lee2Yu-Chul Park3Department of Smart Regional Innovation, Kangwon National University, Gangwon-do, Chuncheon-si 24341, KoreaGeoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, KoreaDepartment of Smart Regional Innovation, Kangwon National University, Gangwon-do, Chuncheon-si 24341, KoreaDepartment of Geophysics, Kangwon National University, Gangwon-do, Chuncheon-si 24341, KoreaThe 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.https://www.mdpi.com/2072-4292/13/6/1196Gangneung-sigroundwater potential mappingSVRGISmachine learningmetaheuristic algorithm |
| spellingShingle | Muhammad Fulki Fadhillah Saro Lee Chang-Wook Lee Yu-Chul Park Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South Korea Remote Sensing Gangneung-si groundwater potential mapping SVR GIS machine learning metaheuristic algorithm |
| title | Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South Korea |
| title_full | Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South Korea |
| title_fullStr | Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South Korea |
| title_full_unstemmed | Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South Korea |
| title_short | Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South Korea |
| title_sort | application of support vector regression and metaheuristic optimization algorithms for groundwater potential mapping in gangneung si south korea |
| topic | Gangneung-si groundwater potential mapping SVR GIS machine learning metaheuristic algorithm |
| url | https://www.mdpi.com/2072-4292/13/6/1196 |
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