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June 21, 2026Discover SustainabilityOpen Access

Evaluation of groundwater vulnerability to pollution using GIS based DRASTIC LU and machine learning techniques in the western catchment of the Abaya Chamo Lakes Basin Ethiopia rift system

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Authors

TWTilahun Wankie WanjalaSHSamuel Dagalo HatiyeAEAbunu Atlabachew Eshete

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Overview

Randomized trial assesses groundwater pollution vulnerability in Ethiopia, highlighting areas at risk.

Key Points

  • The study aims to assess groundwater vulnerability to pollution in the Abaya-Chamo Lakes Basin.
  • Evaluated groundwater vulnerability using DRASTIC-LU and machine learning methods (Random Forest and Support Vector Machine).
  • Integrated eight parameters (depth to groundwater table, net recharge, aquifer media, soil media, topography, vadose zone media, hydraulic conductivity, land use) using a GIS interface.
  • Used AUC-ROC curve to evaluate model performance based on measured nitrate levels.
  • RF-based model achieved prediction accuracy of 0.89, outperforming SVM model with 0.84 accuracy.
  • Groundwater vulnerability classified into five levels from very low to very high based on DRASTIC-LU-RF predictions.
  • Nitrate concentration in vulnerable areas ranged from 21.41 to 53.60 mg/L, indicating high pollution risk.

Cite This Study

Wanjala et al. (2026) studied this question.

synapsesocial.com/papers/6a37800c24f042ddf4c5a43ehttps://doi.org/10.1007/s43621-026-03748-y
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