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Improving multidimensional normal cloud model to evaluate groundwater quality with grey wolf optimization algorithm

Jiaheng Yan1, Ke Ren2, Tao Wang3

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|February 14, 2024
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Summary
This summary is machine-generated.

This study introduces a new multidimensional normal cloud model (GWOPPC model) for groundwater quality evaluation, improving accuracy and efficiency. The GWOPPC model offers a more conservative and robust assessment of groundwater resources.

Keywords:
Cloud modelGroundwater qualityOptimization algorithmProjection pursuit method

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Data Science

Background:

  • Groundwater quality assessment involves inherent uncertainties and ambiguities.
  • Existing multidimensional normal cloud models often overlook indicator weighting schemes.
  • Accurate groundwater quality evaluation is crucial for environmental research and resource management.

Purpose of the Study:

  • To develop a novel multidimensional normal cloud model (GWOPPC model) for integrated groundwater quality evaluation.
  • To address the limitations of previous models by incorporating an improved weighting scheme.
  • To enhance the accuracy and robustness of groundwater quality assessments.

Main Methods:

  • Combined the projection-pursuit (PP) method with the Grey Wolf Optimization (GWO) algorithm to construct the GWOPPC model.
  • Integrated multiple evaluation parameters and simplified the modeling process.
  • Analyzed the effectiveness and robustness of the GWOPPC model against traditional methods.

Main Results:

  • The GWOPPC model demonstrated faster convergence and greater stability compared to other optimization algorithms.
  • The GWO algorithm achieved convergence within 20 iterations with high accuracy.
  • The GWOPP weighting scheme considers sample distribution density, outperforming traditional entropy and principal component weights.
  • The GWOPPC model provided more conservative evaluations compared to fuzzy, Random Forest, and WQI methods.

Conclusions:

  • The GWOPPC model offers a more accurate, efficient, and robust approach to groundwater quality evaluation.
  • This method effectively handles the randomness and ambiguity in groundwater data.
  • The GWOPPC model can be applied to groundwater quality assessment in diverse regions, aiding resource management.