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March 7, 2026WaterOpen Access

Optimized Groundwater Vulnerability Assessment Using Machine Learning: A Case Study of Luyi County, China

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Authors

CLChengdong LiuMWMingming WangHMHeng Ma

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Overview

Assessment models predict groundwater vulnerability in Luyi County, indicating significant pollution risks.

Key Points

  • The study aims to evaluate groundwater vulnerability using machine learning algorithms in Luyi County, China.
  • Applied three machine learning algorithms: Random Forest, XGBoost, and Support Vector Machine.
  • Established classification models targeting nitrate nitrogen concentrations above 10 mg/L.
  • Conducted performance evaluation using multiple metrics including Area Under the Curve (AUC).
  • All models showed strong predictive performance with AUC values between 0.91 and 0.94 and accuracy above 86.5%.
  • Higher correlation was observed between predicted vulnerability and monitored NO3–N concentrations compared to traditional methods.
  • Feature importance analysis indicated aquifer hydraulic conductivity as the key factor in groundwater vulnerability.

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/69abc1b45af8044f7a4ea9a7https://doi.org/10.3390/w18050624
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