Mapping of groundwater protection zones using expert-driven and machine learning methods: a case study of Yulin City, China
Osman Ilniyaz
Groundwater protection is critical for sustainable water resource management, particularly in arid regions. However, current zoning methods show challenges such as data bias of expert-driven models and limited interpretability of machine learning models. To address these issues, using 16 hydrological datasets from Yulin City in northwest China, two methodological frameworks were constructed: one combining the traditional Analytic Hierarchy Process (AHP) with Geographic Information System (GIS),