TITLE:
Groundwater Potential Mapping in the Sissili Sub-Catchment, Burkina Faso: Comparing Multicriteria Analysis and Artificial Intelligence in a Crystalline Basement Aquifer
AUTHORS:
Tatiana Stella Kiswendsida Yameogo, Martine Diallo/Kone, Ludwig Ralf, Benjamin Bonkoungou, Julien Adounkpe
KEYWORDS:
Analytical Hierarchy Process, Artificial Neural Network, Groundwater Potential Zones, Remote Sensing, Sissili Sub-Catchment
JOURNAL NAME:
Journal of Water Resource and Protection,
Vol.17 No.6,
June
12,
2025
ABSTRACT: Groundwater is an essential resource for rural dwellers in Burkina Faso, a country with limited surface water availability. However, localising and accessing groundwater is challenging. This research illustrates the use of Remote Sensing (RS), Geographic Information System (GIS), Analytical Hierarchical Process (AHP), and Artificial Neural Networks (ANN) to determine groundwater potential zones in the Sissili sub-basin. The AHP and ANN processes, using nine (9) selected factors affecting groundwater availability, were mapped and reclassified using a descriptive scale. The two maps obtained included five classifications: very low, low, moderate, high, and very high. The AHP model classified 16.36% (1236.43 km2) as very low, 51.53% (3895.39 km2) as low, 29.24% (2209.89 km2) as moderate, 2.82% (213.17 km2) as high and 0.05% (4.12 km2) as very high. For very low, low, moderate, high, and very high, the ANN model classified 43.56% (3292.98 km2), 14.60% (1103.82 km2), 31.10% (2350.86 km2), 9.10% (687.82 km2), and 1.63% (123.52 km2) of the area respectively. The results were validated using the borehole yield and the Area Under the Curve (AUC). The ANN map results have demonstrated higher accuracy, becoming the most suitable groundwater potential zone delineation method.