Geodetector based statistical and machine learning approach for sustainable water resources within the gunabay watershed in the upper Blue Nile Basin, Ethiopia

• Main influencing factors of sustainable water resource management. • Soil, evapotranspiration, and precipitation greatly impact the recharge process. • Groundwater occurrence strongly relates to transmissivity, recharge and lineament density. • A comprehensive framework to assess groundwater contamination. Availability of sufficient and clean water is a major concern for ensuring food security in Ethiopia. Recently, the estimation of water balance, identification of water resources, and assessment of water quality have gained attention due to advancements in statistical and machine learning methods. This research explores the application of geodetector to quantify the main influencing factors of sustainable water resource management in the Gunabay watershed of upper Blue Nile Basin. The major recharge influencing factors were quantified as evapotranspiration (0.21), elevation (0.16), rainfall (0.20), groundwater depth (0.07), soil (0.78), slope (0.11), wind speed (0.06), temperature (0.17), and land cover (0.08). The results indicated that soil, evapotranspiration, and precipitation exert the maximum impact on recharge process. Groundwater potential influencing factors are quantified as transmissivity (0.48), recharge (0.26), lineament density (0.26), lithology (0.13), drainage density (0.12), and geomorphology (0.06). The Geodetector values illustrates a robust relationship between groundwater occurrence associated with transmissivity, recharge, and lineament density. The factors influencing groundwater quality deterioration were ranked as lithology (0.05), land cover (0.06), population density (0.09), temperature (0.11), recharge (0.16), and soil (0.32). Loamy sand and sandy loam are more susceptible to influencing water quality within the watershed. A high infiltration rate can maximize the level of contamination in the groundwater. Geodetector helps quantify the strength of the interaction between the independent and dependent variables, providing a comprehensive framework for water policymakers and managers. The findings can enhance water resource management and address future sustainable food scarcity while promoting water and soil conservation.