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Related Experiment Video

Updated: Jun 7, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Optimized groundwater quality evaluation using unsupervised machine learning, game theory and Monte-Carlo simulation.

Yuting Yan1, Yunhui Zhang2, Shiming Yang1

  • 1Yibin Research Institute, Southwest Jiaotong University, 644000, China; Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, 611756, China.

Journal of Environmental Management
|November 12, 2024
PubMed
Summary
This summary is machine-generated.

Groundwater quality in SW China is a concern, with over 37% unsuitable for drinking and 66% for irrigation. High nitrate levels pose significant health risks, especially to children, necessitating reduced fertilizer use for sustainable water management.

Keywords:
Clustering analysisGroundwater qualityHydrochemistryProbabilistic health risk assessmentSensitivity analysis

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Area of Science:

  • Hydrogeology
  • Environmental Science
  • Water Resource Management

Background:

  • Groundwater quality assessment is crucial for sustainable development.
  • Traditional hydrochemical analysis methods face challenges in comprehensive evaluation.
  • Unsupervised machine learning offers advanced solutions for complex water quality studies.

Purpose of the Study:

  • To analyze hydrochemical processes influencing groundwater in the Sichuan Basin.
  • To evaluate drinking and irrigation water quality and associated health risks.
  • To apply advanced machine learning and statistical methods for novel groundwater assessment.

Main Methods:

  • Unsupervised machine learning (Self-Organizing Map with K-means) for groundwater classification.
  • Combined-Weights Water Quality Index (CWQI) for drinking water evaluation.
  • Monte-Carlo simulations for probabilistic health risk assessment.

Main Results:

  • Three distinct groundwater types identified: Ca-HCO3, mixed HCO3-dominated, and Ca-Cl/Ca-Mg-Cl.
  • 62.37% of groundwater is suitable for drinking; 33.34% for irrigation.
  • Significant health risks identified, with nitrate concentration being a primary driver, particularly for children.

Conclusions:

  • Carbonate dissolution and silicate weathering are key hydrochemical processes.
  • Intensive agriculture contributes to high nitrate levels, impacting water quality and health.
  • Reducing nitrogen-based fertilizer application is vital for improving groundwater quality and mitigating health risks.