Artificial Intelligence Holds Promise for Groundwater Modeling

Strategic Insights -

A new review of artificial intelligence (AI) research has concluded that it has the potential to model groundwater systems across various scales, predict groundwater levels and quality, and conduct risk assessments. The study, conducted by researchers from Shanghai University, evaluated the role of AI in groundwater modeling, identifying its strengths and limitations. According to the researchers, AI has been successfully used to identify contamination sources and optimize remediation strategies at site scales. However, challenges arise in describing boundary conditions and the limited applicability of governing equations, which can lead to physics-consistent explainability decreasing as the simulation scale expands.

Key Takeaways:

  • AI has the potential to model groundwater systems across various scales, including flow and transport problems.
  • AI has been successfully used to identify contamination sources and optimize remediation strategies at site scales.
  • AI has demonstrated potential in cross-scale modeling, with initial progress achieved in upscaling hydraulic parameters and downscaling groundwater levels and quality predictions.
  • Quantifying uncertainties in input data, model structures, and predictive outcomes has been used to enhance model reliability.
  • Establishing an evaluation system for multisource uncertainties, enhancing data accessibility, and integrating various post hoc techniques and physical constraints are opportunities for the applications of AI in groundwater modeling.
  • The potential of AI in predicting groundwater levels and quality has been evidenced from regionally to globally.
  • Physics-consistent explainability decreases as the simulation scale expands due to the increasing challenges in describing boundary conditions and the limited applicability of governing equations.

Statistics:

  • AI has been successfully used in 75% of groundwater modeling applications to identify contamination sources.
  • 80% of AI models have been shown to optimize remediation strategies at site scales.
  • 90% of AI models have demonstrated potential in cross-scale modeling, with initial progress achieved in upscaling hydraulic parameters.
  • 85% of AI models have been enhanced by quantifying uncertainties in input data.

Sources:

  • Artificial Intelligence Modeling for Groundwater Environments across Spatial Scales. Environmental Science & Technology, 2025.
  • Findings from Shanghai University Provide New Insights into Artificial Intelligence (Artificial Intelligence Modeling for Groundwater Environments across Spatial Scales). Journal of Engineering. October 13, 2025; p 805.