Groundwater Estimation and Determination of its Probable Recharge Source in Lower Swat District, Khyber Pakhtunkhwa, Pakistan by using Analytical Data and Multiple Machine

Imran Ahmad¹Ibrar Ul Haq¹Mansoor Ahmad¹Iram Gul^2*Mursaleen Khan¹Khushnuma Khushnuma¹Ubaid Ullah¹Maqsood Ur Rahman³Mohamed Metwaly^4*

• ¹University of Malakand, Chakdara, Khyber Pakhtunkhwa, Pakistan
• ²National University of Sciences and Technology (NUST), Islamabad, Pakistan
• ³China university of geosciences, Guangzhou, Guangzhou, China
• ⁴King Saud University, Riyadh, Riyadh, Saudi Arabia

This study addresses the lack of integrated hydrogeochemical and machine learning approaches in groundwater assessment, particularly in complex mountainous terrains like Lower Swat District, Pakistan. It aims to identify recharge sources using a combination of analytical data and advanced Machine Learning (ML) algorithms. Groundwater recharge sources and demarcation of feasible exploration sites via actual field data and machine learning based approaches in Lower Swat district were carried out. Based on variations in subsurface lithological composition (e.g., relative proportions of gravel, clay, silt, and bedrock) and the varying distances of selected well sites from the Swat River, the study area was divided into seven zones. Water samples were collected from surface runoff (river, canals) and groundwater (wells, springs) and analyzed for various physico-chemical parameters including major and trace elements to find the probable recharge source in the flood plain area of Swat River. X-ray Fluorescence (XRF) analysis of the rock samples collected from the spring's host were also performed to compare its mineral constituents with the dissolved load of the analyzed groundwater samples. Analytical data interpretation reveals that the recharge source for groundwater in the flood plain regime is River Swat, while infiltration and percolation of rainwater act as a probable recharge source in the mountainous and elevated areas. Acceptable similarities were observed in the geochemical composition of the rock samples, spring's water samples and representative wells in their immediate neighborhood. A linear relationship was observed between the water table and distance from River Swat illustrates that water depth in wells increases with increasing distance from the main recharge source. The study applied six ML models, including Random Forest, Support Vector Machine (SVM), and Ridge Regression, to predict groundwater zones, with Random Forest achieving the highest accuracy (R² = 0.95, RMSE = 8.49, MAE = 4.03), followed by Decision Tree (R² = 0.93). These metrics validate the precision of our groundwater mapping and recharge zone predictions. This integrated approach improves groundwater exploration strategies and supports sustainable water resource management. Furthermore, predicted zones for potential water wells were marked in model wells by using AI, machine learning techniques.