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Simultaneous identification of groundwater contamination source and simulation model parameters based on the rime

Xiao Wang1,2,3, Wenxi Lu4,5,6, Zibo Wang1,2,3

  • 1Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China.

Environmental Monitoring and Assessment
|August 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for groundwater contamination source identification (GCSI) using a one-dimensional convolutional neural network (1DCNN) and the rime optimization algorithm (RIME). The method accurately identifies contamination sources and aquifer parameters, improving remediation efforts.

Keywords:
Groundwater contamination sources identificationOne-dimensional convolutional neural networkRime optimization algorithmSimulation optimization methodsSurrogate model

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Machine Learning

Background:

  • Groundwater contamination source identification (GCSI) is crucial for effective site remediation and liability assessment.
  • Existing simulation optimization methods for GCSI struggle with surrogate model accuracy and local optima in optimization.
  • Accurate identification of contamination source characteristics (location, release history) and aquifer parameters is essential.

Purpose of the Study:

  • To propose an innovative framework for groundwater contamination source identification (GCSI) by integrating advanced machine learning and optimization algorithms.
  • To enhance the accuracy and robustness of GCSI by employing a one-dimensional convolutional neural network (1DCNN) as a surrogate model.
  • To validate the proposed framework's effectiveness in simultaneously identifying contamination source characteristics and aquifer parameters.

Main Methods:

  • Developed a novel GCSI framework embedding a one-dimensional convolutional neural network (1DCNN) as an integrated surrogate component.
  • Employed the rime optimization algorithm (RIME) to solve the composite optimization model for simultaneous parameter and source identification.
  • Validated the framework using a hypothetical case study and compared its performance against existing methods.

Main Results:

  • The 1DCNN surrogate model achieved a high R-squared value of 0.9998, outperforming FCNN and SVR, and maintained R-squared above 0.9993 under ±20% noise.
  • The RIME algorithm demonstrated superior performance over PSO, GA, and EVO, with an average relative error of 8.88% for single identification and 5.88% across 100 trials.
  • The proposed framework successfully achieved simultaneous identification of contamination source characteristics and aquifer parameters.

Conclusions:

  • The integrated 1DCNN and RIME framework offers a robust and accurate approach for groundwater contamination source identification.
  • The method's ability to escape local optima and converge to global solutions makes it highly effective for complex hydrogeological problems.
  • This approach holds significant potential for broader applications in contaminant transport and heterogeneous aquifer characterization.