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Watershed Planning within a Quantitative Scenario Analysis Framework
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Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model

Ata Allah Nadiri1, Zahra Sedghi1, Rahman Khatibi2

  • 1Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.

The Science of the Total Environment
|March 26, 2017
PubMed
Summary

New Supervised Committee Fuzzy Logic (SCFL) models improve aquifer vulnerability mapping. These Artificial Neural Network-integrated models offer a better alternative to the basic DRASTIC framework for assessing contamination risks in multiple aquifers.

Keywords:
Fuzzy logic (SFLGroundwater vulnerability indicesLFL)MFLMultiple aquifers (confined and unconfined)Multiple modelsSCFL model

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Area of Science:

  • Hydrogeology
  • Environmental Science
  • Artificial Intelligence

Background:

  • Assessing aquifer vulnerability is crucial for managing groundwater contamination risks.
  • The DRASTIC framework is a widely used tool, but its effectiveness can be limited with non-point anthropogenic contaminants.
  • Integrating advanced modeling techniques can enhance the accuracy of vulnerability assessments.

Purpose of the Study:

  • To investigate the mapping of aquifer Vulnerability Indices (VI) for multiple aquifers.
  • To integrate the DRASTIC framework with multiple models, including Artificial Neural Networks (ANN) and Fuzzy Logic (FL).
  • To formulate and evaluate Supervised Committee Fuzzy Logic (SCFL) models as an alternative to the basic DRASTIC framework.

Main Methods:

  • The study integrated the DRASTIC framework with multiple models, including Artificial Neural Networks (ANN).
  • Fuzzy Logic (FL) based models such as Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), and Larsen Fuzzy Logic (LFL) were formulated.
  • Supervised Committee (SC) machines were combined with Fuzzy Logic (FL) to create SCFL models.

Main Results:

  • Fuzzy Logic and multiple models significantly improved the correlation between modeled vulnerability indices and observed nitrate-N values.
  • The SCFL multiple models demonstrated improved performance compared to the basic DRASTIC framework.
  • The study provides evidence that SCFL models can serve as a viable alternative for assessing vulnerability in multiple aquifers.

Conclusions:

  • The SCFL multiple models offer a more accurate and reliable approach to mapping aquifer vulnerability indices.
  • These advanced models enhance the assessment of contamination risks, particularly in complex hydrogeological settings with multiple aquifers.
  • The findings support the adoption of integrated AI and FL approaches for improved groundwater resource management.