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Groundwater resource management in Africa, characterized by population growth and rainfall variability, is highly costly. This study used the analytic hierarchy process (AHP) and GIS to identify groundwater potential (GWP) areas in Kano State, Northern Nigeria. Parameters such as land-use-land-cover, drainage density, slope, rainfall, static water level, soil, vadose lithology, and aquifer lithology were selected for GWP analysis. AHP was used to determine parameter weights. This involves experts' rankings based on the parameters’ contribution to GWP. The parameters were then integrated using the weighted overlay tool in ArcGIS 10.5 to produce a GWP map of the study area. The result shows that rainfall and slope are the most important factors contributing 23.9 and 19.4% to the GWP respectively. Land-use-land-cover and soil contribute 4.99 and 5.03% respectively, exerting the least impact. According to the result, the study area can be divided into very low area (1320 km2, 6.60%), low area (4164 km2. 20.70%), moderate area 12,482 km2, 62.0%), high area (12,482 km2, 62.0%) and very high area (382 km2, 1.90%). Model validation gives a Pearson correlation coefficient of 71.3%, which signifies good prediction accuracy.

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Analytic Hierarchy Process and GIS for Groundwater Potential Assessment in Kano State of Northern Nigeria

  • 会议论文

  • 发表日期：

    2025年

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  • Abdulmutallib Ahmad Saidu,
  • Salisu Dan’azumi,
  • Ali Aldrees,
  • Salahu Mohammed Hamza

摘要

Groundwater resource management in Africa, characterized by population growth and rainfall variability, is highly costly. This study used the analytic hierarchy process (AHP) and GIS to identify groundwater potential (GWP) areas in Kano State, Northern Nigeria. Parameters such as land-use-land-cover, drainage density, slope, rainfall, static water level, soil, vadose lithology, and aquifer lithology were selected for GWP analysis. AHP was used to determine parameter weights. This involves experts' rankings based on the parameters’ contribution to GWP. The parameters were then integrated using the weighted overlay tool in ArcGIS 10.5 to produce a GWP map of the study area. The result shows that rainfall and slope are the most important factors contributing 23.9 and 19.4% to the GWP respectively. Land-use-land-cover and soil contribute 4.99 and 5.03% respectively, exerting the least impact. According to the result, the study area can be divided into very low area (1320 km2, 6.60%), low area (4164 km2. 20.70%), moderate area 12,482 km2, 62.0%), high area (12,482 km2, 62.0%) and very high area (382 km2, 1.90%). Model validation gives a Pearson correlation coefficient of 71.3%, which signifies good prediction accuracy.