61 citations as recorded by crossref.

1. Groundwater level prediction using machine learning algorithms in a drought-prone area Q. Pham et al. 10.1007/s00521-022-07009-7
2. WITHDRAWN: Mapping the groundwater memory across Ireland: a step towards a groundwater drought susceptibility assessment P. Schuler et al. 10.1016/j.hydroa.2022.100131
3. A combined hydrodynamic model and deep learning method to predict water level in ungauged rivers G. Li et al. 10.1016/j.jhydrol.2023.130025
4. Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts M. Ali et al. 10.1016/j.jhydrol.2020.124647
5. Causes and implications of groundwater depletion in India: A review S. Dangar et al. 10.1016/j.jhydrol.2021.126103
6. Water-table-driven greenhouse gas emission estimates guide peatland restoration at national scale J. Koch et al. 10.5194/bg-20-2387-2023
7. 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. 10.1289/EHP14171
8. A hybrid approach: remote sensing, analytic hierarchy process and geographic information system for groundwater potential mapping in Dediapada, India G. Kiran et al. 10.1007/s11356-024-35666-9
9. Assessing spatial transferability of a random forest metamodel for predicting drainage fraction E. Bjerre et al. 10.1016/j.jhydrol.2022.128177
10. Enhancing Urban Sustainability and Resilience: Employing Digital Twin Technologies for Integrated WEFE Nexus Management to Achieve SDGs A. Shehadeh et al. 10.3390/su16177398
11. Impacts of land use, climate change and hydrological model structure on nitrate fluxes: Magnitudes and uncertainties I. Seidenfaden et al. 10.1016/j.scitotenv.2022.154671
12. Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest S. He et al. 10.1016/j.chemosphere.2021.133388
13. A data-driven method for the estimation of shallow landslide runout A. Giarola et al. 10.1016/j.catena.2023.107573
14. Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California V. Agarwal et al. 10.1016/j.scitotenv.2022.161138
15. 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. 10.1016/j.jhydrol.2023.130535
16. Groundwater level dynamics in a subtropical fan delta region and its future prediction using machine learning tools: Sustainable groundwater restoration S. Mahammad et al. 10.1016/j.ejrh.2023.101385
17. 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. 10.1016/j.jhydrol.2023.129861
18. The potential of data driven approaches for quantifying hydrological extremes S. Hauswirth et al. 10.1016/j.advwatres.2021.104017
19. Regional Agroclimate Characteristic and Its Multiple Teleconnections: A Case Study in the Jianghan Plain (JHP) Region W. Li et al. 10.3390/w13192789
20. Modeling and digital mapping of shallow water table depth using satellite-based spectral and thermal data: Introducing a framework for digital shallow water table mapping M. Jeihouni et al. 10.1016/j.still.2024.106317
21. Machine-learning-based downscaling of modelled climate change impacts on groundwater table depth R. Schneider et al. 10.5194/hess-26-5859-2022
22. 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. 10.1016/j.watres.2023.120346
23. Upscaling approach to help determine variability in riparian lowland flow dynamics S. Noorduijn et al. 10.1016/j.jhydrol.2023.129536
24. Mapping the groundwater memory across Ireland: A step towards a groundwater drought susceptibility assessment P. Schuler et al. 10.1016/j.jhydrol.2022.128277
25. 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
26. 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
27. 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
28. Estimation of Daily Water Table Level with Bimonthly Measurements in Restored Ombrotrophic Peatland S. Gutierrez Pacheco et al. 10.3390/su13105474
29. 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
30. 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
31. Effective groundwater level recovery from mining reduction: Case study of Baoding and Shijiazhuang Plain area T. Nan et al. 10.26599/JGSE.2023.9280023
32. 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
33. 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
34. A New Digital Twin for Climate Change Adaptation, Water Management, and Disaster Risk Reduction (HIP Digital Twin) H. Henriksen et al. 10.3390/w15010025
35. Application of Hybrid Prediction Methods in Spatial Assessment of Inland Excess Water Hazard A. Laborczi et al. 10.3390/ijgi9040268
36. 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
37. Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms A. Rahman et al. 10.1016/j.advwatres.2020.103595
38. 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
39. Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study R. Gonzalez & J. Arsanjani 10.3390/ijgi10110792
40. Groundwater level forecasting with machine learning models: A review K. Boo et al. 10.1016/j.watres.2024.121249
41. Simulation of regional groundwater levels in arid regions using interpretable machine learning models Q. Liu et al. 10.1016/j.scitotenv.2022.154902
42. 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
43. A robust objective function for calibration of groundwater models in light of deficiencies of model structure and observations R. Schneider et al. 10.1016/j.jhydrol.2022.128339
44. A high-resolution map of diffuse groundwater recharge rates for Australia S. Lee et al. 10.5194/hess-28-1771-2024
45. Hydrological process knowledge in catchment modelling – Lessons and perspectives from 60 years development J. Refsgaard et al. 10.1002/hyp.14463
46. 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
47. 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
48. 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
49. Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence S. Garai & R. Paul 10.1016/j.iswa.2023.200202
50. 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
51. 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
52. 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
53. 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
54. Machine learning for predicting shallow groundwater levels in urban areas A. LaBianca et al. 10.1016/j.jhydrol.2024.130902
55. A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk M. Rohde et al. 10.3389/feart.2021.784499
56. Downscaling a national hydrological model to subgrid scale S. Noorduijn et al. 10.1016/j.jhydrol.2021.126796
57. 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
58. 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
59. 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
60. 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
61. 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