Groundwater Potential Mapping in the Sissili Sub-Catchment, Burkina Faso: Comparing Multicriteria Analysis and Artificial Intelligence in a Crystalline Basement Aquifer — Oak Academic Publishing
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Groundwater Potential Mapping in the Sissili Sub-Catchment, Burkina Faso: Comparing Multicriteria Analysis and Artificial Intelligence in a Crystalline Basement Aquifer
Doctoral Research Program in Climate Change and Water Resources, Applied Hydrology Laboratory (LHA) National Water Institute, University of Abomey Calavi, Cotonou, Benin
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Institute for Research in Applied Sciences and Technology, Ouagadougou, Burkina Faso
Competence Center, West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Ouagadougou, Burkina Faso
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Doctoral Research Program in Climate Change and Water Resources, Applied Hydrology Laboratory (LHA) National Water Institute, University of Abomey Calavi, Cotonou, Benin
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Laboratory of Applied Ecology, Faculty of Agricultural Sciences (FSA), University of Abomey-Calavi (UAC), Cotonou, Benin Republic
1 Doctoral Research Program in Climate Change and Water Resources, Applied Hydrology Laboratory (LHA) National Water Institute, University of Abomey Calavi, Cotonou, Benin
2 Institute for Research in Applied Sciences and Technology, Ouagadougou, Burkina Faso
4 Competence Center, West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Ouagadougou, Burkina Faso
5 Doctoral Research Program in Climate Change and Water Resources, Applied Hydrology Laboratory (LHA) National Water Institute, University of Abomey Calavi, Cotonou, Benin
6 Laboratory of Applied Ecology, Faculty of Agricultural Sciences (FSA), University of Abomey-Calavi (UAC), Cotonou, Benin Republic
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 km 2 ) as very low, 51.53% (3895.39 km 2 ) as low, 29.24% (2209.89 km 2 ) as moderate, 2.82% (213.17 km 2 ) as high and 0.05% (4.12 km 2 ) as very high. For very low, low, moderate, high, and very high, the ANN model classified 43.56% (3292.98 km 2 ), 14.60% (1103.82 km 2 ), 31.10% (2350.86 km 2 ), 9.10% (687.82 km 2 ), and 1.63% (123.52 km 2 ) 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.
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