Research progress of AI-driven groundwater numerical modeling

WEI Yaqiang; CHEN Yiran; CHEN Yuling; ZHAN Zi; ZHANG Jiao; LIANG Jian; XIE Jialun

doi:10.16152/j.cnki.xdxbzr.2025-03-012

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Research progress of AI-driven groundwater numerical modeling

更新时间：2025-06-26

- Research progress of AI-driven groundwater numerical modeling
- Journal of Northwest University (Natural Science Edition) Vol. 55, Issue 3, Pages: 647-657(2025)
- 作者机构：
  
  上海大学　环境与化学工程学院，上海　 200444
- 作者简介：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2025-03-012
  CLC： P641
- Received：25 April 2025，
  
  Published：25 June 2025
- 稿件说明：
移动端阅览
WEI Yaqiang, CHEN Yiran, CHEN Yuling, et al. Research progress of AI-driven groundwater numerical modeling[J]. Journal of northwest university (natural science edition), 2025, 55(3): 647-657.
DOI：

WEI Yaqiang, CHEN Yiran, CHEN Yuling, et al. Research progress of AI-driven groundwater numerical modeling[J]. Journal of northwest university (natural science edition), 2025, 55(3): 647-657. DOI： 10.16152/j.cnki.xdxbzr.2025-03-012.

摘要

地下水是维系生态安全与可持续发展的关键资源，正面临水量波动与水质污染的双重挑战。基于行为过程的数值模型虽可刻画地下水渗流与污染物运移过程，但对参数精度依赖高、计算复杂，难以适应复杂异质环境下的动态模拟需求。人工智能（AI）技术在非线性建模、预测优化与高维特征提取方面展现出独特优势，为突破复杂系统建模瓶颈提供新手段。文章系统综述了AI在地下水模拟中的研究进展，涵盖水位预测、污染迁移模拟与修复优化等关键应用。结果表明，AI模型在动态预测、污染识别与修复方案优化等方面表现良好，混合模型方法在复杂变量交互建模中表现出较强鲁棒性，而深度学习框架在时空特征提取方面具有显著优势。然而，AI模型仍存在泛化能力弱、缺乏物理一致性等问题。未来，应重点聚焦以下几方面的研究：①构建多尺度数据融合与尺度迁移机制，增强模型稳定性与适应性；②提升同尺度模型的可迁移性与复用性，降低对目标场地数据的依赖；③推动从“大数据”向“有效数据”范式转变，强化小样本条件下的建模能力；④通过嵌入物理约束提升替代模型的可信性与物理一致性；⑤构建集成物联网与边缘计算的智能系统，实现地下水的高效感知、模拟与实时决策，助力地下水管理迈向智能化新时代。

Abstract

Groundwater is a critical resource for maintaining ecological security and sustainable development

yet it faces dual challenges of fluctuant quantity and deteriorating quality. While process-based models can describe groundwater flow and contaminant transport

they are highly dependent on precise parameter inputs and computationally intensive

making them less suited for dynamic simulations in complex

heterogeneous environments. Artificial Intelligence (AI) technologies

with their strengths in nonlinear modeling

predictive optimization

and high-dimensional feature extraction

offer novel solutions to overcome bottlenecks in complex system modeling. This article provides a comprehensive review of recent advancements in AI applications for groundwater modeling

covering key areas such as water level prediction

contaminant transport simulation

and remediation optimization. The results indicate that AI models perform well in dynamic forecasting

pollutant identification

and optimization of remediation strategies. Hybrid modeling approaches demonstrate strong robustness in modeling complex variable interactions

while deep learning frameworks show significant advantages in spatiotemporal feature extraction. However

AI models still face challenges such as limited generalization capabilities and a lack of physical consistency. Future research should focus on the following aspects: ① developing multi-scale data fusion and scale-transfer mechanisms to enhance model stability and adaptability; ② Improving the transferability and reusability of same-scale models

with reduced reliance on data from the target site; ③ shifting the paradigm from "big data" to "effective data" to strengthen modeling capabilities under small-sample conditions; ④ embedding physical constraints to improve the reliability and physical consistency of surrogate models; ⑤ constructing intelligent systems that integrate the Internet of Things and edge computing to enable efficient groundwater sensing

modeling

and real-time decision-making

thereby advancing groundwater management into a new era of intelligent operation.

关键词

Keywords

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