93 citations as recorded by crossref.

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3. Assessing spatial transferability of a random forest metamodel for predicting drainage fraction E. Bjerre et al. https://doi.org/10.1016/j.jhydrol.2022.128177
4. High resolution estimates of water table depth across Brazil: insights into model behavior and data challenges M. Fleury et al. https://doi.org/10.1088/1748-9326/ae9bd7
5. Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest S. He et al. https://doi.org/10.1016/j.chemosphere.2021.133388
6. Temperature predictive modeling using machine learning for thermal parameter design of Chinese solar greenhouses Q. Ma et al. https://doi.org/10.1016/j.applthermaleng.2026.131538
7. Spatial and temporal dynamics of plant water source distribution in China H. Chen et al. https://doi.org/10.1016/j.rse.2025.114843
8. A data-driven method for the estimation of shallow landslide runout A. Giarola et al. https://doi.org/10.1016/j.catena.2023.107573
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10. Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California V. Agarwal et al. https://doi.org/10.1016/j.scitotenv.2022.161138
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12. Reconstructing high-resolution groundwater level data using a hybrid random forest model to quantify distributed groundwater changes in the Indus Basin A. Arshad et al. https://doi.org/10.1016/j.jhydrol.2023.130535
13. Response mechanism of groundwater dynamics to Freeze–thaw process in seasonally frozen soil areas: A comprehensive analysis from site to regional scale H. Lyu et al. https://doi.org/10.1016/j.jhydrol.2023.129861
14. Assessment of groundwater variability using ARIMA, random forest, and LSTM-RNN in the northeastern region of Bangladesh A. Nury et al. https://doi.org/10.2166/h2oj.2025.010
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19. Effect and genesis of soil nitrogen loading and hydrogeological conditions on the distribution of shallow groundwater nitrogen pollution in the North China Plain Q. Xia et al. https://doi.org/10.1016/j.watres.2023.120346
20. Mapping the groundwater memory across Ireland: A step towards a groundwater drought susceptibility assessment P. Schuler et al. https://doi.org/10.1016/j.jhydrol.2022.128277
21. Exploring the spatiotemporal water quality variations and their influencing factors in a large floodplain lake in China B. Li et al. https://doi.org/10.1016/j.ecolind.2020.106454
22. Evaluation of shallow groundwater dynamics after water supplement in North China Plain based on attention-GRU model T. Nan et al. https://doi.org/10.1016/j.jhydrol.2023.130085
23. Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation S. Samantaray & A. Sahoo https://doi.org/10.1016/j.gsd.2024.101178
24. Comparing alternative conceptual models for tile drains and soil heterogeneity for the simulation of tile drainage in agricultural catchments V. Boico et al. https://doi.org/10.1016/j.jhydrol.2022.128120
25. High Resolution Water Table Modeling of the Shallow Groundwater Using a Knowledge-Guided Gradient Boosting Decision Tree Model J. Koch et al. https://doi.org/10.3389/frwa.2021.701726
26. Active learning framework for landslide-induced impulse wave risk assessment using GAN-enhanced kriging and adaptive cooperative PSO W. Xu et al. https://doi.org/10.1016/j.enggeo.2026.108779
27. Groundwater level forecasting with machine learning models: A review K. Boo et al. https://doi.org/10.1016/j.watres.2024.121249
28. Simulation of regional groundwater levels in arid regions using interpretable machine learning models Q. Liu et al. https://doi.org/10.1016/j.scitotenv.2022.154902
29. Deciphering the impact of cascade reservoirs on nitrogen transport and nitrate transformation: Insights from multiple isotope analysis and machine learning Y. Bao et al. https://doi.org/10.1016/j.watres.2024.122638
30. A robust objective function for calibration of groundwater models in light of deficiencies of model structure and observations R. Schneider et al. https://doi.org/10.1016/j.jhydrol.2022.128339
31. A high-resolution map of diffuse groundwater recharge rates for Australia S. Lee et al. https://doi.org/10.5194/hess-28-1771-2024
32. Hydrological process knowledge in catchment modelling – Lessons and perspectives from 60 years development J. Refsgaard et al. https://doi.org/10.1002/hyp.14463
33. Machine-Learning-Based Historical Reconstruction of Soil Organic Carbon Dynamics in Coastal Tidal Flats: Quantifying the Spatiotemporal Impacts of Reclamation C. Kou et al. https://doi.org/10.3390/rs18070978
34. Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation M. Sattari et al. https://doi.org/10.5194/hess-25-603-2021
35. Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence S. Garai & R. Paul https://doi.org/10.1016/j.iswa.2023.200202
36. Spatial Analysis and Risk Assessment Model Research of Arthritis Based on Risk Factors: China, 2011, 2013 and 2015 Z. Ren et al. https://doi.org/10.1109/ACCESS.2020.3037912
37. Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm S. Samantaray et al. https://doi.org/10.1016/j.clwat.2024.100003
38. Quantifying shallow groundwater table uncertainty from sensor density and placement in very dense networks: A case study R. Forchhammer et al. https://doi.org/10.2166/hydro.2026.078
39. Machine learning for predicting shallow groundwater levels in urban areas A. LaBianca et al. https://doi.org/10.1016/j.jhydrol.2024.130902
40. Downscaling a national hydrological model to subgrid scale S. Noorduijn et al. https://doi.org/10.1016/j.jhydrol.2021.126796
41. Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems G. Kopsiaftis et al. https://doi.org/10.3389/frwa.2023.1195029
42. Estimation of groundwater age distributions from hydrochemistry: comparison of two metamodelling algorithms in the Heretaunga Plains aquifer system, New Zealand C. Tschritter et al. https://doi.org/10.5194/hess-27-4295-2023
43. Groundwater level prediction using machine learning algorithms in a drought-prone area Q. Pham et al. https://doi.org/10.1007/s00521-022-07009-7
44. WITHDRAWN: Mapping the groundwater memory across Ireland: a step towards a groundwater drought susceptibility assessment P. Schuler et al. https://doi.org/10.1016/j.hydroa.2022.100131
45. Exploring stable isotope patterns in monthly precipitation across Southeast Asia using contemporary deep learning models and SHapley Additive exPlanations (SHAP) techniques M. Heydarizad et al. https://doi.org/10.1080/10256016.2025.2508811
46. Integrating climate resilience and digital twin technologies in housing: A comparative analysis and sustainable design proposal for Jordan and South Africa A. Shehadeh et al. https://doi.org/10.1016/j.rineng.2025.108409
47. Hybrid ensemble machine learning for groundwater level prediction in data-scarce northern Bangladesh M. Mamun et al. https://doi.org/10.1080/1573062X.2026.2649885
48. Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh M. Rahman & M. Biswas https://doi.org/10.31127/tuje.1808553
49. A combined hydrodynamic model and deep learning method to predict water level in ungauged rivers G. Li et al. https://doi.org/10.1016/j.jhydrol.2023.130025
50. Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts M. Ali et al. https://doi.org/10.1016/j.jhydrol.2020.124647
51. Causes and implications of groundwater depletion in India: A review S. Dangar et al. https://doi.org/10.1016/j.jhydrol.2021.126103
52. Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data E. Petermann et al. https://doi.org/10.1289/EHP14171
53. Enhancing Urban Sustainability and Resilience: Employing Digital Twin Technologies for Integrated WEFE Nexus Management to Achieve SDGs A. Shehadeh et al. https://doi.org/10.3390/su16177398
54. Impacts of land use, climate change and hydrological model structure on nitrate fluxes: Magnitudes and uncertainties I. Seidenfaden et al. https://doi.org/10.1016/j.scitotenv.2022.154671
55. The significant role of vegetation activity in regulating wetland methane emission in China B. Liang et al. https://doi.org/10.1016/j.envres.2025.120773
56. Development of a model to estimate groundwater recharge M. Ali https://doi.org/10.26599/JGSE.2025.9280062
57. The role of secondary data in estimating groundwater levels in the Iberian Peninsula N. Ben-Salem et al. https://doi.org/10.1016/j.gsd.2026.101594
58. SMamba-KAN: an advanced temporal-nonlinear model for precise water level prediction X. Yan et al. https://doi.org/10.3389/frwa.2025.1638839
59. Spatiotemporal Reconstruction of Groundwater Depletion in India’s Oldest Coalfield Using Machine Learning and GIS, With InSAR and Fractal Analysis Insights D. Ghosh et al. https://doi.org/10.1109/JSTARS.2026.3731412
60. Groundwater level dynamics in a subtropical fan delta region and its future prediction using machine learning tools: Sustainable groundwater restoration S. Mahammad et al. https://doi.org/10.1016/j.ejrh.2023.101385
61. Spatial Decoupling of Climatic Recharge Opportunity and Groundwater Response in an Alluvial Fan: Implications for Regional Managed Aquifer Recharge Screening A. Ali et al. https://doi.org/10.3390/w18182335
62. Mapping the spatial transferability of knowledge-guided machine learning: Application to the prediction of drain flow fraction R. Schneider et al. https://doi.org/10.1016/j.scitotenv.2024.178314
63. Regional Agroclimate Characteristic and Its Multiple Teleconnections: A Case Study in the Jianghan Plain (JHP) Region W. Li et al. https://doi.org/10.3390/w13192789
64. Tackling water table depth modeling via machine learning: From proxy observations to verifiability J. Janssen et al. https://doi.org/10.1016/j.advwatres.2025.104955
65. Accurate groundwater level predictions: integration of physics-driven and machine learning approaches G. Shuai et al. https://doi.org/10.1007/s13201-026-02862-6
66. Upscaling approach to help determine variability in riparian lowland flow dynamics S. Noorduijn et al. https://doi.org/10.1016/j.jhydrol.2023.129536
67. Spatial prediction of groundwater levels using machine learning and geostatistical models: a case study of coastal faulted aquifer systems in southeastern Tunisia H. Chihi & I. Ben Cheikh Larbi https://doi.org/10.1007/s10040-023-02686-7
68. Data challenges in large-scale transient groundwater modeling using machine learning Y. Ma et al. https://doi.org/10.1016/j.advwatres.2026.105453
69. A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction M. Bordoni et al. https://doi.org/10.3390/w13091208
70. Estimation of Daily Water Table Level with Bimonthly Measurements in Restored Ombrotrophic Peatland S. Gutierrez Pacheco et al. https://doi.org/10.3390/su13105474
71. Groundwater level estimation in northern region of Bangladesh using hybrid locally weighted linear regression and Gaussian process regression modeling A. Elbeltagi et al. https://doi.org/10.1007/s00704-022-04037-0
72. Effective groundwater level recovery from mining reduction: Case study of Baoding and Shijiazhuang Plain area T. Nan et al. https://doi.org/10.26599/JGSE.2023.9280023
73. Assessing impacts of land use/land cover patterns to shallow groundwater nitrate pollution in an agricultural-dominant area in northwest China using random forest F. Su et al. https://doi.org/10.1007/s12665-024-11856-z
74. A New Digital Twin for Climate Change Adaptation, Water Management, and Disaster Risk Reduction (HIP Digital Twin) H. Henriksen et al. https://doi.org/10.3390/w15010025
75. Enhanced Urban Heat Island Modeling with Machine Learning and Regression Kriging in a Topographically Diverse Medium-Sized City I. Holobaca et al. https://doi.org/10.3390/land15010049
76. Application of Hybrid Prediction Methods in Spatial Assessment of Inland Excess Water Hazard A. Laborczi et al. https://doi.org/10.3390/ijgi9040268
77. Performance and Limitations of Machine Learning Models for Groundwater Level Prediction Using Hydro-Climatic Variables N. Zeydalinejad et al. https://doi.org/10.1007/s41748-026-01281-6
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79. Machine Learning-Based Reconstruction and Prediction of Groundwater Time Series in the Allertal, Germany T. Tran et al. https://doi.org/10.3390/w17030433
80. Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study R. Gonzalez & J. Arsanjani https://doi.org/10.3390/ijgi10110792
81. Development of data-driven statistical models for daily water table depth prediction and variable selection: Two case studies in coastal plain forested wetlands A. Manna et al. https://doi.org/10.1080/23737484.2026.2630287
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84. A critical review on groundwater level depletion monitoring based on GIS and data-driven models: Global perspectives and future challenges M. Monir et al. https://doi.org/10.1016/j.hydres.2024.05.001
85. Employing machine learning to quantify long-term climatological and regulatory impacts on groundwater availability in intensively irrigated regions S. Nozari et al. https://doi.org/10.1016/j.jhydrol.2022.128511
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88. A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk M. Rohde et al. https://doi.org/10.3389/feart.2021.784499
89. Using random forests to explore the feasibility of groundwater knowledge transfer between the contiguous US and Denmark Y. Ma et al. https://doi.org/10.1088/2515-7620/ad9b08
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