60 citations as recorded by crossref.

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23. Mapping the groundwater memory across Ireland: A step towards a groundwater drought susceptibility assessment P. Schuler et al. 10.1016/j.jhydrol.2022.128277
24. 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 10.1007/s10040-023-02686-7
25. A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction M. Bordoni et al. 10.3390/w13091208
26. Exploring the spatiotemporal water quality variations and their influencing factors in a large floodplain lake in China B. Li et al. 10.1016/j.ecolind.2020.106454
27. Estimation of Daily Water Table Level with Bimonthly Measurements in Restored Ombrotrophic Peatland S. Gutierrez Pacheco et al. 10.3390/su13105474
28. Evaluation of shallow groundwater dynamics after water supplement in North China Plain based on attention-GRU model T. Nan et al. 10.1016/j.jhydrol.2023.130085
29. Groundwater level estimation in northern region of Bangladesh using hybrid locally weighted linear regression and Gaussian process regression modeling A. Elbeltagi et al. 10.1007/s00704-022-04037-0
30. Effective groundwater level recovery from mining reduction: Case study of Baoding and Shijiazhuang Plain area T. Nan et al. 10.26599/JGSE.2023.9280023
31. Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation S. Samantaray & A. Sahoo 10.1016/j.gsd.2024.101178
32. 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. 10.1007/s12665-024-11856-z
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34. Application of Hybrid Prediction Methods in Spatial Assessment of Inland Excess Water Hazard A. Laborczi et al. 10.3390/ijgi9040268
35. Comparing alternative conceptual models for tile drains and soil heterogeneity for the simulation of tile drainage in agricultural catchments V. Boico et al. 10.1016/j.jhydrol.2022.128120
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37. High Resolution Water Table Modeling of the Shallow Groundwater Using a Knowledge-Guided Gradient Boosting Decision Tree Model J. Koch et al. 10.3389/frwa.2021.701726
38. Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study R. Gonzalez & J. Arsanjani 10.3390/ijgi10110792
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41. Deciphering the impact of cascade reservoirs on nitrogen transport and nitrate transformation: Insights from multiple isotope analysis and machine learning Y. Bao et al. 10.1016/j.watres.2024.122638
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43. A high-resolution map of diffuse groundwater recharge rates for Australia S. Lee et al. 10.5194/hess-28-1771-2024
44. Hydrological process knowledge in catchment modelling – Lessons and perspectives from 60 years development J. Refsgaard et al. 10.1002/hyp.14463
45. The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting S. Hauswirth et al. 10.5194/hess-27-501-2023
46. Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation M. Sattari et al. 10.5194/hess-25-603-2021
47. A critical review on groundwater level depletion monitoring based on GIS and data-driven models: Global perspectives and future challenges M. Monir et al. 10.1016/j.hydres.2024.05.001
48. Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence S. Garai & R. Paul 10.1016/j.iswa.2023.200202
49. Employing machine learning to quantify long-term climatological and regulatory impacts on groundwater availability in intensively irrigated regions S. Nozari et al. 10.1016/j.jhydrol.2022.128511
50. Spatial Analysis and Risk Assessment Model Research of Arthritis Based on Risk Factors: China, 2011, 2013 and 2015 Z. Ren et al. 10.1109/ACCESS.2020.3037912
51. Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm S. Samantaray et al. 10.1016/j.clwat.2024.100003
52. Altimetry‐Derived Gravity Gradients Using Spectral Method and Their Performance in Bathymetry Inversion Using Back‐Propagation Neural Network X. Wan et al. 10.1029/2022JB025785
53. Machine learning for predicting shallow groundwater levels in urban areas A. LaBianca et al. 10.1016/j.jhydrol.2024.130902
54. A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk M. Rohde et al. 10.3389/feart.2021.784499
55. Downscaling a national hydrological model to subgrid scale S. Noorduijn et al. 10.1016/j.jhydrol.2021.126796
56. Basin-wide tracking of nitrate cycling in Yangtze River through dual isotope and machine learning F. Xie et al. 10.1016/j.scitotenv.2023.169656
57. Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems G. Kopsiaftis et al. 10.3389/frwa.2023.1195029
58. Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns J. Bikše et al. 10.1016/j.jhydrol.2023.129424
59. Estimation of groundwater age distributions from hydrochemistry: comparison of two metamodelling algorithms in the Heretaunga Plains aquifer system, New Zealand C. Tschritter et al. 10.5194/hess-27-4295-2023
60. Prediction of groundwater level using the hybrid model combining wavelet transform and machine learning algorithms A. Wei et al. 10.1007/s12145-022-00853-0