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Related Experiment Video

Updated: Nov 6, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

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A GIS-based groundwater pollution potential using DRASTIC, modified DRASTIC, and bivariate statistical models.

Khabat Khosravi1, Majid Sartaj2, Mahshid Karimi3

  • 1Department of Watershed Management Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. Khabat.khosravi@gmail.com.

Environmental Science and Pollution Research International
|May 7, 2021
PubMed
Summary
This summary is machine-generated.

This study assessed groundwater vulnerability in Iran using multiple models, finding the weights-of-evidence (WOE) model most effective. High pollution potential is concentrated in southern urban areas, highlighting critical regions for groundwater protection.

Keywords:
DRASTICEBFFRGroundwater vulnerabilityNitrateWOE

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Geospatial Analysis

Background:

  • Groundwater contamination poses a significant threat to water resources, necessitating accurate vulnerability assessments.
  • Traditional methods like DRASTIC may not always capture complex contaminant transport dynamics.
  • Statistical bivariate models offer alternative approaches for predicting groundwater pollution potential.

Purpose of the Study:

  • To evaluate and compare the performance of DRASTIC, modified DRASTIC, and three statistical bivariate models (frequency ratio, evidential belief function, weights-of-evidence) for groundwater vulnerability assessment.
  • To identify areas with high groundwater pollution potential in the Sari-Behshahr plain, Iran.
  • To validate the predictive accuracy of the applied models using the receiver operating characteristic (ROC) method.

Main Methods:

  • Employed DRASTIC, modified DRASTIC, frequency ratio (FR), evidential belief function (EBF), and weights-of-evidence (WOE) models.
  • Utilized nitrate concentration data from 109 wells (training dataset: 76, testing dataset: 33) for modeling and validation.
  • Generated five groundwater potential pollution (GPP) maps and evaluated them using the ROC method.

Main Results:

  • The weights-of-evidence (WOE) model demonstrated the highest predictive power, outperforming EBF, FR, modified DRASTIC, and DRASTIC.
  • Groundwater potential pollution maps indicated that high and very high vulnerability zones are predominantly located in the southern, urbanized parts of the study area.
  • Model performance varied, with statistical bivariate methods generally showing superior predictive capabilities.

Conclusions:

  • The weights-of-evidence model is highly effective for groundwater vulnerability assessment in the study region.
  • Urban areas in the southern Sari-Behshahr plain are critical hotspots for groundwater contamination risk.
  • The methodology can be applied to other regions to identify and manage groundwater contaminant-prone areas effectively.