Evaluation of Groundwater Potential Zones Using GIS-Based Machine Learning Ensemble Models in the Gidabo Watershed, Ethiopia.

ABSTRACT: The main objective of this study is to map and evaluate groundwater potential zones (GWPZs) using advanced ensemble machine learning (ML) models, notably Random Forest (RF) and Support Vector Machine (SVM). GWPZs are identified by considering essential factors such as geology, drainage density, slope, land use/land cover (LULC), rainfall, soil, and lineament density. This is combined with datasets used for training and validating the RF and SVM models, which consisted of 75 potential sites (boreholes and springs), 22 non-potential sites (bare lands and settlement areas), and 20 potential sites (water bodies). Each dataset is randomly partitioned into two sets: training (70%) and validation (30%). The model's performance is evaluated using the area under the receiver operating characteristi

SUBMITTER: Mussa MM 

PROVIDER: S-EPMC11637779 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature