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Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms.

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This study enhances groundwater contamination vulnerability assessment using artificial intelligence. A deep learning model significantly improved accuracy over traditional methods for nitrate contamination prediction.

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Artificial Intelligence

Background:

  • Groundwater resource management requires accurate aquifer contamination vulnerability assessment.
  • The original DRASTIC model (ODM) has limitations in subjectivity and robustness for nitrate contamination.
  • Enhancing vulnerability assessment is crucial for groundwater conservation.

Purpose of the Study:

  • To improve groundwater contamination vulnerability assessment using advanced AI and optimization algorithms.
  • To overcome the limitations of the original DRASTIC model for nitrate contamination.
  • To develop a more accurate and robust vulnerability index.

Main Methods:

  • Proposed two-level modeling strategies incorporating artificial intelligence.
  • Strategy 1: Used particle swarm optimization (PSO) and differential evolution (DE) to optimize DRASTIC parameters, creating ODVI-PSO and ODVI-DE indices.
  • Strategy 2: Employed a deep learning neural networks (DLNN) model using Strategy 1 indices as input for enhanced vulnerability assessment.

Main Results:

  • The deep learning neural networks (DLNN) model in Strategy 2 demonstrated superior performance in vulnerability assessment.
  • Validated for nitrate values, the adjusted vulnerability index from Strategy 2 showed higher accuracy.
  • The DLNN model effectively extracted additional information from the ODVI-PSO and ODVI-DE indices.

Conclusions:

  • Strategy 2, utilizing DLNN, provides higher accuracy for aquifer contamination vulnerability modeling.
  • The proposed AI-driven approach offers efficient applicability for groundwater contamination vulnerability assessment.
  • This research highlights the potential of advanced computational techniques in hydrogeological studies.