Spatial assessment of groundwater potential using Quantum GIS and multi-criteria decision analysis (QGIS-AHP) in the Sawla-Tuna-Kalba district of Ghana

Abstract

Study region: Sawla-Tuna-Kalba (4226 km2). Study focus: This study employed QGIS to assess the groundwater potential in the Sawla-Tuna-Kalba district of Ghana with some selected surficial factors while estimating the groundwater recharge from 1981 to 2021. New hydrological insights: Among the classification algorithms tested, Random Forest (RF) yielded the highest overall accuracy with 93.63% while Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) had 90.22% and 84.73% respectively. From the AHP model, geology had the highest weight of 0.279. It was found that low potential regions comprise 229.53 km2, moderate zones comprise 1700.62 km2, high potential zones comprise 2135.04 km2, and excellent potential areas were found to be 152.14 km2. The groundwater recharge due to rainfall computed from the Chaturvedi formula indicates that an average of 2.85% of the total annual rainfall goes to groundwater recharge with a variation of ± 0.35 mm. The total recharge for 2021 was found to be 30.18 ± 0.35 mm but between 1981 and 2021 the total recharge was 1191.18 ± 0.35 mm. Also, from the soil analysis, it was found that 56.21% of the study area would allow infiltration. In conclusion, it was observed that the groundwater potential in the study area was high and can be attributed to the recharge and present surficial conditions.

Keywords

• Groundwater potential
• Geology
• Remote sensing
• Quantum GIS
• Recharge