Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques
Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and...
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| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/7/1200 |
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| author | Sunmin Lee Yunjung Hyun Saro Lee Moung-Jin Lee |
| author_facet | Sunmin Lee Yunjung Hyun Saro Lee Moung-Jin Lee |
| author_sort | Sunmin Lee |
| collection | DOAJ |
| description | Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test groundwater yield potential in Yangpyeong-gun, South Korea. Groundwater control factors derived from remote sensing data were used for mapping, including nine topographic factors, two hydrological factors, forest type, soil material, land use, and two geological factors. A total of 53 well locations with both specific capacity (SPC) data and transmissivity (T) data were selected and randomly divided into two classes for model training (70%) and testing (30%). First, the frequency ratio (FR) was calculated for SPC and T, and then the boosted classification tree (BCT) method of the machine learning model was applied. In addition, an ensemble model, FR-BCT, was applied to generate and compare groundwater potential maps. Model performance was evaluated using the receiver operating characteristic (ROC) method. To test the model, the area under the ROC curve was calculated; the curve for the predicted dataset of SPC showed values of 80.48% and 87.75% for the BCT and FR-BCT models, respectively. The accuracy rates from T were 72.27% and 81.49% for the BCT and FR-BCT models, respectively. Both the BCT and FR-BCT models measured the contributions of individual groundwater control factors, which showed that soil was the most influential factor. The machine learning techniques used in this study showed effective modeling of groundwater potential in areas where data are relatively scarce. The results of this study may be used for sustainable development of groundwater resources by identifying areas of high groundwater potential. |
| first_indexed | 2024-03-10T20:35:58Z |
| format | Article |
| id | doaj.art-8e7c1bfac86f4034be6d53844a09b223 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:35:58Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8e7c1bfac86f4034be6d53844a09b2232023-11-19T21:02:35ZengMDPI AGRemote Sensing2072-42922020-04-01127120010.3390/rs12071200Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning TechniquesSunmin Lee0Yunjung Hyun1Saro Lee2Moung-Jin Lee3Department of Geoinformatics, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul 02504, KoreaDepartment of Land and Water Environment Research, Korea Environment Institute (KEI), 370 Sicheong-daero, Sejong-si 30147, KoreaGeoscience Platform Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, KoreaCenter for Environmental Data Strategy, Korea Environment Institute (KEI), 370 Sicheong-daero, Sejong-si 30147, KoreaAdequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test groundwater yield potential in Yangpyeong-gun, South Korea. Groundwater control factors derived from remote sensing data were used for mapping, including nine topographic factors, two hydrological factors, forest type, soil material, land use, and two geological factors. A total of 53 well locations with both specific capacity (SPC) data and transmissivity (T) data were selected and randomly divided into two classes for model training (70%) and testing (30%). First, the frequency ratio (FR) was calculated for SPC and T, and then the boosted classification tree (BCT) method of the machine learning model was applied. In addition, an ensemble model, FR-BCT, was applied to generate and compare groundwater potential maps. Model performance was evaluated using the receiver operating characteristic (ROC) method. To test the model, the area under the ROC curve was calculated; the curve for the predicted dataset of SPC showed values of 80.48% and 87.75% for the BCT and FR-BCT models, respectively. The accuracy rates from T were 72.27% and 81.49% for the BCT and FR-BCT models, respectively. Both the BCT and FR-BCT models measured the contributions of individual groundwater control factors, which showed that soil was the most influential factor. The machine learning techniques used in this study showed effective modeling of groundwater potential in areas where data are relatively scarce. The results of this study may be used for sustainable development of groundwater resources by identifying areas of high groundwater potential.https://www.mdpi.com/2072-4292/12/7/1200groundwater potentialspecific capacitymachine learningboosted treeensemble models |
| spellingShingle | Sunmin Lee Yunjung Hyun Saro Lee Moung-Jin Lee Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques Remote Sensing groundwater potential specific capacity machine learning boosted tree ensemble models |
| title | Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques |
| title_full | Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques |
| title_fullStr | Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques |
| title_full_unstemmed | Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques |
| title_short | Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques |
| title_sort | groundwater potential mapping using remote sensing and gis based machine learning techniques |
| topic | groundwater potential specific capacity machine learning boosted tree ensemble models |
| url | https://www.mdpi.com/2072-4292/12/7/1200 |
| work_keys_str_mv | AT sunminlee groundwaterpotentialmappingusingremotesensingandgisbasedmachinelearningtechniques AT yunjunghyun groundwaterpotentialmappingusingremotesensingandgisbasedmachinelearningtechniques AT sarolee groundwaterpotentialmappingusingremotesensingandgisbasedmachinelearningtechniques AT moungjinlee groundwaterpotentialmappingusingremotesensingandgisbasedmachinelearningtechniques |