Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper Egypt
Abstract The effects of climate change and rapid population growth increase the demand for freshwater, particularly in arid and hyper-arid environments, considering that groundwater is an essential water resource in these regions. The main focus of this research was to generate a groundwater potenti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-02-01
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| Series: | Geoscience Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40562-023-00261-2 |
| _version_ | 1811165826959540224 |
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| author | Hesham Morgan Ahmed Madani Hussien M. Hussien Tamer Nassar |
| author_facet | Hesham Morgan Ahmed Madani Hussien M. Hussien Tamer Nassar |
| author_sort | Hesham Morgan |
| collection | DOAJ |
| description | Abstract The effects of climate change and rapid population growth increase the demand for freshwater, particularly in arid and hyper-arid environments, considering that groundwater is an essential water resource in these regions. The main focus of this research was to generate a groundwater potential map in the Center Eastern Desert, Egypt, using a random forest classification machine learning model. Based on satellite data, geological maps and field survey, fifteen effective features influencing groundwater potentiality were created. These effective features include elevation, slope angle, slope aspect, terrain ruggedness index, curvature, lithology, lineament density, distance from major fractures, topographic wetness index, stream power index, drainage density, rainfall, as well as distance from rivers and channels, soil type and land use/land cover. Collinearity analysis was used for feature selection. A 100 dependent points (57 water points and 43 non-potential mountainous areas) were labeled and classified according to hydrogeological conditions in the three main aquifers (Basement, Nubian and Quaternary Aquifers) in the study area. The random forest algorithm was trained using (70%) of the dependent points. Then, it was validated using (30%) and the hyper-parameters were optimized. Groundwater potential map was predicted and classified as good (5.1%), moderate (0.1%), poor (4.2%) and non-potentiality (90.6%). Sensitivity (92%), F1-score (94%) and accuracy (97%) are validation methods used due to the imbalanced dataset problem. The most important effective features for groundwater potential map were determined based on the random forest and the receiver operating characteristics curve. Groundwater management sustainability was discussed based on the predicted groundwater potential map and aquifer conditions. Therefore, the random forest model is helpful for delineating groundwater potential zones and can be used in similar locations all over the world. |
| first_indexed | 2024-04-10T15:43:59Z |
| format | Article |
| id | doaj.art-313ba38a232b4c4ea7008017a6047786 |
| institution | Directory Open Access Journal |
| issn | 2196-4092 |
| language | English |
| last_indexed | 2024-04-10T15:43:59Z |
| publishDate | 2023-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Geoscience Letters |
| spelling | doaj.art-313ba38a232b4c4ea7008017a60477862023-02-12T12:14:04ZengSpringerOpenGeoscience Letters2196-40922023-02-0110111910.1186/s40562-023-00261-2Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper EgyptHesham Morgan0Ahmed Madani1Hussien M. Hussien2Tamer Nassar3Department of Geology, Faculty of Science, Cairo UniversityDepartment of Geology, Faculty of Science, Cairo UniversityGeology Department, Desert Research CenterDepartment of Geology, Faculty of Science, Cairo UniversityAbstract The effects of climate change and rapid population growth increase the demand for freshwater, particularly in arid and hyper-arid environments, considering that groundwater is an essential water resource in these regions. The main focus of this research was to generate a groundwater potential map in the Center Eastern Desert, Egypt, using a random forest classification machine learning model. Based on satellite data, geological maps and field survey, fifteen effective features influencing groundwater potentiality were created. These effective features include elevation, slope angle, slope aspect, terrain ruggedness index, curvature, lithology, lineament density, distance from major fractures, topographic wetness index, stream power index, drainage density, rainfall, as well as distance from rivers and channels, soil type and land use/land cover. Collinearity analysis was used for feature selection. A 100 dependent points (57 water points and 43 non-potential mountainous areas) were labeled and classified according to hydrogeological conditions in the three main aquifers (Basement, Nubian and Quaternary Aquifers) in the study area. The random forest algorithm was trained using (70%) of the dependent points. Then, it was validated using (30%) and the hyper-parameters were optimized. Groundwater potential map was predicted and classified as good (5.1%), moderate (0.1%), poor (4.2%) and non-potentiality (90.6%). Sensitivity (92%), F1-score (94%) and accuracy (97%) are validation methods used due to the imbalanced dataset problem. The most important effective features for groundwater potential map were determined based on the random forest and the receiver operating characteristics curve. Groundwater management sustainability was discussed based on the predicted groundwater potential map and aquifer conditions. Therefore, the random forest model is helpful for delineating groundwater potential zones and can be used in similar locations all over the world.https://doi.org/10.1186/s40562-023-00261-2Groundwater potential mapImbalanced datasetRandom forestVariable importance |
| spellingShingle | Hesham Morgan Ahmed Madani Hussien M. Hussien Tamer Nassar Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper Egypt Geoscience Letters Groundwater potential map Imbalanced dataset Random forest Variable importance |
| title | Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper Egypt |
| title_full | Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper Egypt |
| title_fullStr | Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper Egypt |
| title_full_unstemmed | Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper Egypt |
| title_short | Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper Egypt |
| title_sort | using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of east esna idfu area nile valley upper egypt |
| topic | Groundwater potential map Imbalanced dataset Random forest Variable importance |
| url | https://doi.org/10.1186/s40562-023-00261-2 |
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