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Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM).

Ata Allah Nadiri1, Maryam Gharekhani1, Rahman Khatibi2

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

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Summary

This study introduces a Supervised Intelligent Committee Machine (SICM) for groundwater vulnerability assessment. SICM enhances Artificial Intelligence (AI) models to better predict contaminant penetration, improving upon traditional methods.

Keywords:
Ardabil aquiferArtificial intelligence modelsNitrateSupervised Intelligent Committee MachineVulnerability index

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Artificial Intelligence

Background:

  • Groundwater vulnerability assessment is crucial for protecting water resources from surface contaminants.
  • The DRASTIC index, based on seven hydrogeological parameters, is a common method but has limitations in reflecting complex aquifer conditions.
  • Artificial Intelligence (AI) models offer potential for improving the accuracy of vulnerability assessments.

Purpose of the Study:

  • To develop and evaluate a Supervised Intelligent Committee Machine (SICM) model for assessing groundwater vulnerability.
  • To integrate Artificial Neural Networks (ANN) with Support Vector Machine (SVM), Neuro-Fuzzy (NF), and Gene Expression Programming (GEP) for enhanced modeling.
  • To improve the prediction of contaminant (nitrate-N) penetration into aquifers by conditioning the DRASTIC index with measured concentrations.

Main Methods:

  • A novel SICM model was developed, utilizing ANN to combine the outputs of SVM, NF, and GEP models.
  • Each AI model was trained using the DRASTIC index parameters and conditioned with measured nitrate-N concentrations.
  • The performance of individual AI models and the hybrid SICM was evaluated against observed nitrate values in the Ardabil aquifer.

Main Results:

  • AI models produced smoother vulnerability index fronts compared to the conventional DRASTIC framework, showing better correlation with observed nitrate values.
  • The SICM model demonstrated superior performance by integrating the strengths of the individual AI models (SVM, NF, GEP).
  • SICM effectively handled aquifer heterogeneity and parameter uncertainty, leading to more reliable vulnerability index estimations.

Conclusions:

  • The SICM model offers a significant advancement in groundwater vulnerability assessment, outperforming individual AI techniques.
  • This hybrid approach provides a more accurate and robust method for identifying areas at risk of contamination.
  • The findings highlight the potential of advanced AI techniques in hydrogeological studies for effective water resource management.