Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper Egypt

• Published in: Geoscience letters 2023-02, Vol.10 (1), p.9-19, Article 9
• Main Authors: Morgan, Hesham, Madani, Ahmed, Hussien, Hussien M., Nassar, Tamer
• Format: Article
• Language: English
• Subjects: Algorithms; Aquifers; Arid environments; Arid zones; Aridity; Atmospheric Sciences; Biogeosciences; Climate change; Climate effects; Collinearity; Deserts; Distance; Drainage density; Earth and Environmental Science; Earth Sciences; Fractures; Freshwater; Geological mapping; Geological maps; Geological surveys; Geology; Geophysics/Geodesy; Groundwater; Groundwater management; Groundwater potential; Groundwater potential map; Hydrogeology; Imbalanced dataset; Inland water environment; Land cover; Land use; Learning algorithms; Lithology; Machine learning; Mountain regions; Mountainous areas; Oceanography; Planetology; Population growth; Quaternary; Rainfall; Random forest; Research Letter; Rivers; Ruggedness; Soil types; Sustainability; Variable importance; Water resources
• ISSN: 2196-4092; 2196-4092
• DOI: 10.1186/s40562-023-00261-2

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.