人工智能驱动的地下水数值模拟研究进展

魏亚强; 陈依然; 陈玉玲; 占杍; 张浇; 梁建; 谢佳伦

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

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人工智能驱动的地下水数值模拟研究进展

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- 人工智能驱动的地下水数值模拟研究进展
- Research progress of AI-driven groundwater numerical modeling
- 西北大学学报（自然科学版） 2025年55卷第3期页码：647-657
- 作者机构：
  
  上海大学　环境与化学工程学院，上海　 200444
- 作者简介：
  
  [ "魏亚强，上海大学副研究员、上海交通大学博士后，中国科学院大学和美国亚利桑那大学联合培养博士。主要从事土壤和地下水多介质污染物迁移转化与模拟研究，在Environmental Science & Technology、Water research、Journal of Hazardous Materials等期刊发表论文40余篇，其中第一或通讯作者20余篇。获得发明专利授权5项，软件著作权6项。主持国家自然科学面上基金、重点基金课题、自然科学青年基金、国家重点研发计划“场地土壤污染成因与治理技术”子课题等。参与完成国家973项目、基金委联合重点项目、国防科工局高放废物地质处置研究开发项目、中国科学院战略性先导科技专项等多项国家级研究课题。现任上海市科委评审专家、上海市生态环境局科研项目专家库专家，Earth Critical Zone期刊编委、Eco-Environment & Health、Agriculture Communications、《西北大学学报（自然科学版）》《生态与农村环境学报》《华东地质》等期刊青年编委。负责生态环境部《地下水污染模拟预测评估工作指南》修订，参与《全国地下水污染防治实施方案》《污染地块地下水修复和风险管控技术导则》编制。" ]
- 基金信息：
  
  国家自然科学基金(42477004;42330706)
- DOI：10.16152/j.cnki.xdxbzr.2025-03-012
  中图分类号： P641
- 收稿日期：2025-04-25，
  
  纸质出版日期：2025-06-25
- 稿件说明：
移动端阅览
魏亚强, 陈依然, 陈玉玲, 等. 人工智能驱动的地下水数值模拟研究进展[J]. 西北大学学报（自然科学版）, 2025,55(3):647-657.

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.
魏亚强, 陈依然, 陈玉玲, 等. 人工智能驱动的地下水数值模拟研究进展[J]. 西北大学学报（自然科学版）, 2025,55(3):647-657. DOI： 10.16152/j.cnki.xdxbzr.2025-03-012.

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

references

ROHDE M M , ALBANO C M , HUGGINS X , et al . Groundwater-dependent ecosystem map exposes global dryland protection needs [J ] . Nature , 2024 , 632 ( 8023 ): 101 - 107 .

KUANG X X , LIU J G , SCANLONB R , et al . The changing nature of groundwater in the global water cycle [J ] . Science , 2024 , 383 ( 6686 ): eadf0630 .

PODGORSKI J , KRACHT O , ARAGUAS-ARAGUAS L , et al . Groundwater vulnerability to pollution in Africa’s Sahel region [J ] . Nature Sustainability , 2024 , 7 : 558 - 567 .

肖燚 , 郭亚会 , 李明蔚 , 等 . 基于机器学习的地下水水质预测研究 [J ] . 北京师范大学学报(自然科学版) , 2022 , 58 ( 2 ): 261 - 268 .

XIAO Y , GUO Y H , LI M W , et al . Machine learning to predict groundwater quality [J ] . Journal of Beijing Normal University (Natural Science) 2022 , 58 ( 2 ): 261 - 268 .

JASECHKO S , SEYBOLD H , PERRONE D , et al . Rapid groundwater decline and some cases of recovery in aquifers globally [J ] . Nature , 2024 , 625 ( 7996 ): 715 - 721 .

PODGORSKI J , BERG M . Global threat of arsenic in groundwater [J ] . Science , 2020 , 368 ( 6493 ): 845 - 850 .

PODGORSKI J , BERG M . Global analysis and prediction of fluoride in groundwater [J ] . Nature Communications , 2022 , 13 ( 1 ): 42 - 32 .

LIU X C , BEUSEN A H W , VAN GRINSVEN H J M , et al . Impact of groundwater nitrogen legacy on water quality [J ] . Nature Sustainability , 2024 , 7 : 891 - 900 .

FAMIGLIETTI J S . The global groundwater crisis [J ] . Nature Climate Change , 2014 , 4 ( 11 ): 945 - 948 .

ZHI W , APPLING A P , GOLDEN H E , et al . Deep learning for water quality [J ] . Nature Water , 2024 , 2 : 228 - 241 .

ZIPKIN E F , ZYLSTRA E R , WRIGHT A D , et al . Addressing data integration challenges to link ecological processes across scales [J ] . Frontiers in Ecology and the Environment , 2021 , 19 ( 1 ): 30 - 38 .

殷乐宜 , 魏亚强 , 陈坚 , 等 . 土壤和地下水耦合数值模拟研究进展 [J ] . 环境保护科学 , 2020 , 46 ( 3 ): 127 - 131 .

YIN L Y , WEI Y Q , CHEN J , et al Research progress of coupled numerical simulation of soil and groundwater [J ] . Environmental Protection Science , 2020 , 46 ( 3 ): 127 - 131 .

魏亚强 , 陈坚 , 张铎 , 等 . 基于Python的地下水模拟研究进展与应用 [J ] . 计算机技术与发展 , 2021 , 31 ( 5 ): 150 - 156 .

WEI Y Q , CHEN J , ZHANG D , et al . Application and research progress of Python in groundwater numerical simulation [J ] . Computer Technology and Development , 2021 , 31 ( 5 ): 150 - 156

MCDONALD M G , HARBAUGH A W . A modular three-dimensional finite-difference ground-water flow model [M ] . Reston : US Geological Survey , 1988 .

LONG D , YANG W T , SCANLON B R , et al . Southto-North Water Diversion stabilizing Beijing’s groundwater levels [J ] . Nature Communications , 2020 , 11 ( 1 ): 3665

RADLOFF K A , ZHENG Y , MICHAEL H A , et al . Arsenic migration to deep groundwater in Bangladesh influenced by adsorption and water demand [J ] . Nature Geoscience , 2011 , 4 ( 11 ): 793 - 798 .

CONNELL L D , VAN DEN DAELE G . A quantitative approach to aquifer vulnerability mapping [J ] . Journal of Hydrology , 2003 , 276 ( 1/2/3/4 ): 71 - 88 .

MIRZA I A , AKRAM M S , SHAH N A , et al . Analytical solutions to the advection-diffusion equation with Atangana-Baleanu time-fractional derivative and a concentrated loading [J ] . Alexandria Engineering Journal , 2021 , 60 ( 1 ): 1199 - 1208 .

WEI Y Q , YUAN C P , XU X Y , et al . Colloid formation and facilitated chromium transport in the coastal area soil induced by freshwater and seawater alternating fluctuations [J ] . Water Research , 2022 , 218 : 118456 .

WEI Y Q , XU X Y , ZHAO L , et al . Numerical modeling investigations of colloid facilitated chromium migration considering variable-density flow during the coastal groundwater table fluctuation [J ] . Journal of Hazardous Materials , 2023 , 443 : 130282 .

WEI Y Q , CAO X D . A COMSOL-PHREEQC coupled Python framework for reactive transport modeling in soil and groundwater [J ] . Ground Water , 2022 , 60 ( 2 ): 284294 .

APPELO C A J , ROLLE M . PHT3D: A reactive multicomponent transport model for saturated porous media [J ] . Ground Water , 2010 , 48 ( 5 ): 627 - 632 .

BOO K B W , EL-SHAFIE A , OTHMAN F , et al . Groundwater level forecasting with machine learning models: A review [J ] . Water Research , 2024 , 252 : 121249 .

KRENN M , POLLICE R , GUO S Y , et al . On scientific understanding with artificial intelligence [J ] . Nature Reviews Physics , 2022 , 4 ( 12 ): 761 - 769 .

REICHSTEIN M , CAMPS-VALLS G , STEVENS B , et al . Deep learning and process understanding for datadriven Earth system science [J ] . Nature , 2019 , 566 ( 7743 ): 195 - 204 .

BUTLER K T , DAVIES D W , CARTWRIGHT H , et al . Machine learning for molecular and materials science [J ] . Nature , 2018 , 559 : 547 - 555 .

KEITH J A , VASSILEV-GALINDO V , CHENG B Q , et al . Combining machine learning and computational chemistry for predictive insights into chemical systems [J ] . Chemical Reviews , 2021 , 121 ( 16 ): 9816 - 9872 .

LIU X , LU D W , ZHANG A Q , et al . Data-driven machine learning in environmental pollution: Gains and problems [J ] . Environmental Science & Technology , 2022 , 56 ( 4 ): 2124 - 2133 .

Advancing geoscience with AI [J ] . Nature Geoscience , 2024 , 17 ( 10 ): 947 .

WUNSCH A , LIESCH T , BRODA S . Deep learning shows declining groundwater levels in Germany until 2100 due to climate change [J ] . Nature Communications , 2022 , 13 ( 1 ): 1221 .

HAGGERTY R , SUN J X , YU H F , et al . Application of machine learning in groundwater quality modeling: A comprehensive review [J ] . Water Research , 2023 , 233 : 119745 .

HANSON B , STALL S , CUTCHER-GERSHENFELD J , et al . Garbage in, garbage out: Mitigating risks and maximizing benefits of AI in research [J ] . Nature , 2023 , 623 ( 7985 ): 28 - 31 .

TAORMINA R , CHAU K W , SETHI R . Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon [J ] . Engineering Applications of Artificial Intelligence , 2012 , 25 ( 8 ): 1670 - 1676 .

REZAIE-BALF M , NAGANNA S R , GHAEMI A , et al . Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting [J ] . Journal of Hydrology , 2017 , 553 : 356 - 373 .

MALAKAR P , MUKHERJEE A , BHANJA S N , et al . Deep learning-based forecasting of groundwater level trends in India: Implications for crop production and drinking water supply [J ] . ACS ES&T Engineering , 2021 , 1 ( 6 ): 965 - 977 .

YIN W J , FAN Z W , TANGDAMRONGSUB N , et al . Comparison of physical and data-driven models to forecast groundwater level changes with the inclusion of GRACE: A case study over the state of Victoria, Australia [J ] . Journal of Hydrology , 2021 , 602 : 126735 .

CAI H J , LIU S N , SHI H Y , et al . Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method [J ] . Journal of Hydrology , 2022 , 613 : 128495 .

SHEIKH KHOZANI Z , BARZEGARI BANADKOOKI F , EHTERAM M , et al . Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level [J ] . Journal of Cleaner Production , 2022 , 348 : 131224 .

AKBARIFARD S , MADADI M R , ZOUNEMAT-KERMANI M . An artificial intelligence-based model for optimal conjunctive operation of surface and groundwater resources [J ] . Nature Communications , 2024 , 15 : 553 .

JING H , HE X , TIAN Y , et al . Comparison and interpretation of data-driven models for simulating site-specific human-impacted groundwater dynamics in the North China Plain [J ] . Journal of Hydrology , 2023 , 616 : 128751 .

冯鹏宇 , 金韬 , 沈一选 , 等 . 基于CNN-Transformer的城区地下水位预测 [J ] . 计算机仿真 , 2023 , 40 ( 4 ): 492 - 498 .

FENG P Y , JIN T , SHEN Y X , et al . Prediction of urban groundwater level based on CNN-transformer [J ] . Computer Simulation , 2023 , 40 ( 4 ): 492 - 498 .

DE GRAAF I E M , GLEESON T , RENS VAN BEEK L H , et al . Environmental flow limits to global groundwater pumping [J ] . Nature , 2019 , 574 ( 7776 ): 90 - 94 .

LI X Y , LONG D , SCANLON B R , et al . Climate change threatens terrestrial water storage over the Tibetan Plateau [J ] . Nature Climate Change , 2022 , 12 : 801 - 807 .

SATIZÁBAL-ALARCÓN D A , SUHOGUSOFF A , FERRARI L C . Characterization of groundwater storage changes in the Amazon River Basin based on downscaling of GRACE/GRACE-FO data with machine learning models [J ] . Science of the Total Environment , 2024 , 912 : 168958 .

KHORRAMI B , ALI S , GÜNDÜZ O . Investigating the local-scale fluctuations of groundwater storage by using downscaled GRACE/GRACE-FO JPL mascon product based on machine learning (ML) algorithm [J ] . Water Resources Management , 2023 , 37 ( 9 ): 3439 - 3456 .

HASAN M F , SMITH R , VAJEDIAN S , et al . Global land subsidence mapping reveals widespread loss of aquifer storage capacity [J ] . Nature Communications , 2023 , 14 ( 1 ): 6180 .

YIN J N , MEDELLÍN-AZUARA J , ESCRIVA-BOU A , et al . Bayesian machine learning ensemble approach to quantify model uncertainty in predicting groundwater storage change [J ] . Science of the Total Environment , 2021 , 769 : 144715 .

郑闯 , 董军 , 张伟红 , 等 . 基于过程模拟的地下水污染源识别研究进展 [J ] . 中国环境科学 , 2024 , 44 ( 9 ): 4999 - 5006 .

ZHENG C , DONG J , ZHANG W H , et al . Research progress of groundwater pollution source identification based on process simulation [J ] . China Environmental Science , 2024 , 44 ( 9 ): 4999 - 5006 .

王晓红 , 魏加华 , 成志能 , 等 . 地下水有机污染源识别技术体系研究与示范 [J ] . 环境科学 , 2013 , 34 ( 2 ): 662 - 667 .

WANG X H , WEl J H , CHENG Z N , et al . Groundwater organic pollution source identification technology system research and application [J ] . Environmental Science , 2013 , 34 ( 2 ): 662 - 667 .

HOU Z Y , LAO W M , WANG Y , et al . Homotopy-based hyper-heuristic searching approach for reciprocal feedback inversion of groundwater contamination source and aquifer parameters [J ] . Applied Soft Computing , 2021 , 104 : 107191 .

SECCI D , MOLINO L , ZANINI A . Contaminant source identification in groundwater by means of artificial neural network [J ] . Journal of Hydrology , 2022 , 611 : 128003 .

BIAN J M , RUAN D M , WANG Y , et al . Bayesian ensemble machine learning-assisted deterministic and stochastic groundwater DNAPL source inversion with a homotopy-based progressive search mechanism [J ] . Journal of Hydrology , 2023 , 624 : 129925 .

LI J H , LU W X , WANG H , et al . Groundwater contamination source identification based on a hybrid particle swarm optimization-extreme learning machine [J ] . Journal of Hydrology , 2020 , 584 : 124657 .

MO S X , ZABARAS N , SHI X Q , et al . Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification [J ] . Water Resources Research , 2019 , 55 ( 5 ): 3856 - 3881 .

XING Z X , QU R Z , ZHAO Y , et al . Identifying the release history of a groundwater contaminant source based on an ensemble surrogate model [J ] . Journal of Hydrology , 2019 , 572 : 501 - 516 .

ZHENG N , LI Z , XIA X M , et al . Estimating line contaminant sources in non-Gaussian groundwater conductivity fields using deep learning-based framework [J ] . Journal of Hydrology , 2024 , 630 : 130727 .

WU Y H , LI M , XIE H J , et al . Characterizing multisource heavy metal contaminated sites at the Hun River basin: An integrated deep learning and data assimilation approach [J ] . Journal of Hydrology , 2025 , 648 : 132349 .

XIA X M , JIANG S M , ZHOU N Q , et al . Groundwater contamination source identification and high-dimensional parameter inversion using residual dense convolutional neural network [J ] . Journal of Hydrology , 2023 , 617 : 129013 .

STACKELBERG P E , BELITZ K , BROWN C J , et al . Machine learning predictions of pH in theglacial aquifer system, northern USA [J ] . Ground Water , 2021 , 59 ( 3 ): 352 - 368

SAHOUR H , GHOLAMI V , VAZIFEDAN M . A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer [J ] . Journal of Hydrology , 2020 , 591 : 125321

SARKAR S , DAS K , MUKHERJEE A . Groundwater salinity across India: Predicting occurrences and controls by field-observations and machine learning modeling [J ] . Environmental Science & Technology , 2024 , 58 ( 8 ): 3953 - 3965 .

TAN Z , YANG Q , ZHENG Y . Machine learning models of groundwater arsenic spatial distribution in Bangladesh: Influence of Holocene sediment depositional history [J ] . Environmental Science & Technology , 2020 , 54 ( 15 ): 9454 - 9463 .

LOMBARD M A , BRYAN M S , JONES D K , et al . Machine learning models of arsenic in private wells throughout the conterminous United States as a tool for exposure assessment in human health studies [J ] . Environmental Science & Technology , 2021 , 55 ( 8 ): 5012 - 5023 .

PODGORSKI J E , LABHASETWAR P , SAHA D , et al . Prediction modeling and mapping of groundwater fluoride contamination throughout India [J ] . Environmental Science & Technology , 2018 , 52 ( 17 ): 9889 - 9898 .

NOLAN B T , FIENEN M N , LORENZ D L . A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA [J ] . Journal of Hydrology , 2015 , 531 : 902 - 911 .

MOTEVALLI A , NAGHIBI S A , HASHEMI H , et al . Inverse method using boosted regression tree and knearest neighbor to quantify effects of point and nonpoint source nitrate pollution in groundwater [J ] . Journal of Cleaner Production , 2019 , 228 : 1248 - 1263 .

黄燕鹏 . 基于机器学习的区域地下水水质时空预测方法研究 [D ] . 哈尔滨 : 哈尔滨工业大学 , 2024 .

GEORGE S , DIXIT A . A machine learning approach for prioritizing groundwater testing for per-and polyfluoroalkyl substances (PFAS) [J ] . Journal of Environmental Management , 2021 , 295 : 113359 .

SONG X H , REN H Y , HOU Z S , et al . Predicting future well performance for environmental remediation design using deep learning [J ] . Journal of Hydrology , 2023 , 617 : 129110 .

ROGERS L L , DOWLA F U , JOHNSON V M . Optimal field-scale groundwater remediation using neural networks and the genetic algorithm [J ] . Environmental Science & Technology , 1995 , 29 ( 5 ): 1145 - 1155 .

SHI L , LI J , PALANSOORIYA K N , et al . Modeling phytoremediation of heavy metal contaminated soils through machine learning [J ] . Journal of Hazardous Materials , 2023 , 441 : 129904 .

YU Q , ZHENG Y , ZHANG P P , et al . Genetic programming-based predictive model for the Cr removal effect of in-situ electrokinetic remediation in contaminated soil [J ] . Journal of Hazardous Materials , 2023 , 460 : 132430 .

LEE J , IM J , KIM U , et al . A data mining approach to predict in situ detoxification potential of chlorinated ethenes [J ] . Environmental Science & Technology , 2016 , 50 ( 10 ): 5181 - 5188 .

曹文庚 , 付宇 , 南天 , 等 . 机器学习在地下水环境背景值与污染风险评价的应用和展望 [J ] . 地质学报 , 2023 , 97 ( 7 ): 2408 - 2424 .

CAO W G , FU Y , NAN T , et al Machine learning model in groundwater background value and pollution risk assessment: Application and prospects [J ] . Acta Geologica Sinica , 2023 , 97 ( 7 ): 2408 - 2424 .

HANOON M S , AHMED A N , FAI C M , et al . Application of artificial intelligence models for modeling water quality in groundwater: Comprehensive review, evaluation and future trends [J ] . Water, Air, & Soil Pollution , 2021 , 232 ( 10 ): 411 .

LI P , ZHA Y , ZHANG Y , et al . Deep learning integrating scale conversion and pedo-transfer function to avoid potential errors in cross-scale transfer [J ] . Water Resources Research , 2024 , 60 ( 3 ): 1 - 21 .

GUNNING D , STEFIK M , CHOI J , et al . XAI-Explainable artificial intelligence [J ] . Science Robotics , 2019 , 4 ( 37 ): eaay7120 .

ZHONG S F , ZHANG K , BAGHERI M , et al . Machine learning: New ideas and tools in environmental science and engineering [J ] . Environmental Science & Technology , 2021 , 55 ( 19 ): 12741 - 12754 .

LI J H , LU W X , LUO J N . Groundwater contamination sources identification based on the Long-Short Term Memory network [J ] . Journal of Hydrology , 2021 , 601 : 126670 .

YADAV B , CH S , MATHUR S , et al . Estimation of in-situ bioremediation system cost using a hybrid Extreme Learning Machine (ELM) -particle swarm optimization approach [J ] . Journal of Hydrology , 2016 , 543 : 373 - 385 .

CAO H L , XIE X J , SHI J B , et al . Siamese network-based transfer learning model to predict geogenic contaminated groundwaters [J ] . Environmental Science & Technology , 2022 , 56 ( 15 ): 11071 - 11079 .

TOKRANOV A K , RANSOM K M , BEXFIELDL M , et al . Predictions of groundwater PFAS occurrence at drinking water supply depths in the United States [J ] . Science , 2024 , 386 ( 6723 ): 748 - 755 .

TARTAKOVSKY A M , MARRERO C O , PERDIKARIS P , et al . Physics-informed deep neural networks for learning parameters and constitutive relationships in subsurface flow problems [J ] . Water Resources Research , 2020 , 56 ( 5 ): 1 - 16 .

ZHAN Z , WEI Y , YEH T C J , et al . Small data insights for groundwater management [J ] . Environmental Science & Technology , 2025 , 59 ( 7 ): 3339 - 3343 .

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