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<p>Groundwater scarcity poses a major challenge in semi-arid regions of Ethiopia, requiring innovative assessment tools for sustainable management. This study introduces a GIS-based fuzzy logic framework to delineate groundwater potential zones in Gubalafto Woreda, integrating slope, drainage density, geology, rainfall, land use/land cover, Channel Network Base Level (CNBL), and topographic indices (TPI, TWI). High-resolution datasets, including 30&#xa0;m DEM, Landsat 8 imagery, and CHIRPS rainfall, were standardized into fuzzy membership values and combined through overlay analysis. Model performance was evaluated using Ordinary Least Squares regression, Moran’s I autocorrelation, and ROC-AUC metrics. Results indicate that 17% of the area exhibits high groundwater potential, primarily associated with gentle slopes, low drainage density, and elevated TWI values. The model achieved an AUC of 0.752, confirming robust predictive accuracy. By integrating fuzzy logic with geospatial analysis, this approach addresses data limitations and provides a replicable tool for groundwater resource mapping. The findings offer actionable insights for policymakers and water managers to optimize resource allocation and enhance resilience in data-scarce, drought-prone environments.</p>

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Utilizing GIS and fuzzy logic for groundwater resource mapping in gubalafto woreda, Ethiopia: a spatial analysis approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月12日

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  • Getanew Sewnetu Zewdu,
  • Asrat Hibu

摘要

Groundwater scarcity poses a major challenge in semi-arid regions of Ethiopia, requiring innovative assessment tools for sustainable management. This study introduces a GIS-based fuzzy logic framework to delineate groundwater potential zones in Gubalafto Woreda, integrating slope, drainage density, geology, rainfall, land use/land cover, Channel Network Base Level (CNBL), and topographic indices (TPI, TWI). High-resolution datasets, including 30 m DEM, Landsat 8 imagery, and CHIRPS rainfall, were standardized into fuzzy membership values and combined through overlay analysis. Model performance was evaluated using Ordinary Least Squares regression, Moran’s I autocorrelation, and ROC-AUC metrics. Results indicate that 17% of the area exhibits high groundwater potential, primarily associated with gentle slopes, low drainage density, and elevated TWI values. The model achieved an AUC of 0.752, confirming robust predictive accuracy. By integrating fuzzy logic with geospatial analysis, this approach addresses data limitations and provides a replicable tool for groundwater resource mapping. The findings offer actionable insights for policymakers and water managers to optimize resource allocation and enhance resilience in data-scarce, drought-prone environments.