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Abstract
Mountainous regions are vital for recharging aquifers in plain areas downstream, but understanding the geological, hydrogeological, and climatic factors is crucial to comprehend groundwater processes in these regions. Several parameters, including lithology, topography, secondary porosity, geological structures, and climatic conditions, affect the potential of groundwater in mountainous aquifers. Traditional groundwater modeling tools face several challenges in handling large amounts of real-time data, such as extracting useful features, quantifying uncertainty, and identifying links between different variables. Recent technological advances in artificial intelligence, particularly machine learning, provide solutions for hydrogeological research and applications. This paper focuses on modeling potential zones of groundwater sources using various methodologies based on GIS, spatial remote sensing, and machine learning. The study evaluated three models, Random Forest, Support Vector Machine, and Logistic Regression, in identifying potential groundwater zones in the Rherhaya watershed. More than 200 localized spring points were needed to ensure efficient model learning. The Support Vector Machine model demonstrated the highest performance during the 70/30% split, with a ROC-AUC of 84.4% for the test data. The study identified four critical conditioning factors of groundwater potentiality, including Topographic position index, River Distance, Valley Depth, and Plane Curvature. The models also highlighted the distance to rivers as a significant factor, particularly in the upstream portion of the watershed. The very low potentiality class occupied the largest area (over 32%), followed by low (between 24 and 29%), moderate (12–19%), high (10–14%), and very high (only 9–12%) classes. Only the Support Vector Machine model predicted that 12% of the catchment area had a high potential for groundwater resources, indicating its superior performance in identifying high-potential zones. The results offer valuable insights that can aid decision-makers in effectively managing water resources in vulnerable areas.
Highlights
Mountainous regions are renowned for their ability to provide 50% of available freshwater (Kohler et al, 2010)
The results of the confusion matrix diagram indicate a low linear correlation between all variables (Fig. 5). These results suggest that the data redundancy of some factors has a minor impact on performance, indicating that all factors must be considered in this analysis
Groundwater potential maps are produced by the (a) random forest (RF) model; (b) logistic regression (LR) model and (c) support vector machine (SVM) model
Summary
Mountainous regions are renowned for their ability to provide 50% of available freshwater (Kohler et al, 2010). Direct access to groundwater outcrops is limited in mountainous regions where groundwater dynamics are deep and complex, making hydrogeological exploration more difficult. These regions are often fractured (such as the Rherhaya watershed in the Atlas Mountains) and contain discontinuous aquifers, which further complicates the evaluation of groundwater potential. The parameters governing this potential in mountainous aquifers are numerous and interdependent, including lithology, geomorphology, topography, secondary porosity, geological structures, fracture density, permeability, drainage patterns and densities, groundwater recharge, water table depth, slope, land use/cover, and climate conditions (Rathay et al, 2018)
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