Abstract

Abstract Groundwater availability is one of the key anxieties in most semi-arid regions of Ethiopia. The purpose of this study was to investigate the groundwater potential zone map of the alluvial plain of Gambela. The study applied analytic hierarchy process (AHP) models with four different machine learning algorithms: random forest classifier (RFC), gradient boosting classifier (GBC), decision tree classifier (DTC), and K-neighbor classifier (KNC). The features that are used as predictors include geology, geomorphology, slope, soil, lineament density, drainage density, land use and land cover (LULC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic roughness index (TRI), and rainfall. The final output of the groundwater potential zone was classified as low, moderate, high, and very high potential zones. The authentication through receiver operating curve (ROC) shows 78.2, 93.4, 92.5, 72.4, and 87.7% values of area under the curve (AUC) for AHP, RFC, GBC, DTC, and KNC, respectively. The results show that RFC and GBC are the best groundwater potential zone (GWPZ) map estimator. The study also shows that rainfall and geomorphology are the primary factors influencing the GWPZ. The outcome might promote improved management alternatives in other areas of the country with a comparable climate.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.