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Updated: Aug 7, 2026
12:44
Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
A Comprehensive Risk Assessment Framework for Groundwater Quality: Integrating Natural Background Levels, Threshold
Vahab Amiri1, Peiyue Li2, Mehdi Torabi-Kaveh1
1Associate Professor of Hydrogeology, Department of Geology, Yazd University, Yazd, Iran.
Summary
This study developed a new framework using statistical methods and machine learning (ML) to assess groundwater quality. It accurately distinguishes between natural and human-caused pollution in the Qazvin aquifer.
Area of Science:
- Environmental Science
- Hydrogeology
- Data Science
Background:
- Groundwater is a vital freshwater source facing increasing anthropogenic impacts.
- Assessing groundwater quality is crucial for sustainable resource management.
Purpose of the Study:
- To introduce an integrated framework for assessing groundwater quality in the Qazvin aquifer.
- To differentiate between geogenic and anthropogenic influences on groundwater chemistry.
Main Methods:
- Employed iterative statistical techniques (Iterative 2σ, CDF, IGT) to determine natural background levels (NBLs) and threshold values (TVs).
- Utilized advanced machine learning (ML) models (SVM, CatBoost) with compositional log-ratio transformation for hydrochemical facies classification.
- Validated statistical methods using ProUCL 5.2.
Main Results:
- The iterative Grubbs test (IGT) effectively identified geogenic baseline groundwater quality.
- ML models, particularly SVM-based ones, achieved high accuracy (>93%) in classifying hydrochemical facies.
- The integrated framework accurately distinguished natural groundwater characteristics from anthropogenic alterations.
Conclusions:
- The combined statistical and ML approach offers a robust and transferable method for groundwater quality assessment.
- This framework supports evidence-based strategies for sustainable aquifer management.
- Understanding geogenic versus anthropogenic impacts is key to effective water resource protection.
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