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Factor weighting in DRASTIC modeling.

F A L Pacheco1, L M G R Pires2, R M B Santos3

  • 1Department of Geology, University of Trás-os-Montes and Alto Douro, Ap 1013, 5001-801 Vila Real, Portugal; Chemistry Research Centre, Vila Real, Portugal.

The Science of the Total Environment
|December 3, 2014
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Summary
This summary is machine-generated.

Comparing five weighting techniques for aquifer vulnerability assessment revealed significant differences in results. Correspondence Analysis emerged as the most suitable method for adjusting the DRASTIC model

Keywords:
Correspondence AnalysisDRASTIC aquifer vulnerability modelFactor weightingLogistic RegressionSensitivity AnalysisSpearman rank-order correlation

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Area of Science:

  • Hydrogeology
  • Environmental Science
  • Water Resource Management

Background:

  • Aquifer vulnerability assessment integrates diverse data (soil, hydrology, geology, water quality) presenting a complex, multidisciplinary challenge.
  • The DRASTIC model is a widely used technique for assessing aquifer vulnerability, calculating an index based on seven weighted factors.
  • Adjustments to DRASTIC's factor weights are common to suit regional specificities, but different adjustment techniques can yield significantly different vulnerability outcomes.

Purpose of the Study:

  • To compare five distinct weighting techniques for the DRASTIC model in aquifer vulnerability assessment.
  • To evaluate the impact of different weighting adjustments on vulnerability index outcomes across 26 aquifer systems in Portugal.
  • To identify the most reliable adjustment technique for aquifer vulnerability assessment.

Main Methods:

  • Employed five weighting techniques: Delphi consensus (original DRASTIC), Sensitivity Analysis, Spearman correlations, Logistic Regression, and Correspondence Analysis.
  • Applied these techniques to 26 aquifer systems in Portugal, analyzing factors like soil texture, recharge, hydraulic conductivity, relief, and nitrate contamination.
  • Compared the resulting vulnerability indices and factor weight distributions from each technique.

Main Results:

  • Most adjustment techniques (except Sensitivity Analysis) elevated weights for soil, hydrologic, aquifer properties, and environmental parameters (≈4.4) while reducing weights for aquifer media (≈1.5).
  • Logistic Regression predicted the highest vulnerability, whereas Sensitivity Analysis predicted the lowest.
  • Vulnerability indices varied by up to 51 points, indicating an uncertainty of 2.5 vulnerability classes.

Conclusions:

  • The choice of weighting technique significantly impacts aquifer vulnerability assessment outcomes, introducing considerable uncertainty.
  • Correspondence Analysis is recommended as the best adjustment technique when weights are proportional to rating ranges.
  • Additional expertise may be necessary for satisfactory selection and application of weighting techniques in vulnerability assessments.