57 citations as recorded by crossref.

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21. Prediction of creep index of soft clays using gene expression programming X. Xue & C. Deng 10.1007/s00500-023-08053-8
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23. A deep neural multi-model ensemble (DNM2E) framework for modelling groundwater levels over Kerala using dynamic variables A. Keerthana & A. Nair 10.1007/s00477-023-02570-6
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25. Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data O. Kisi et al. 10.1007/s11069-017-2767-9
26. Analysis and Prediction of Groundwater Resource Change Under Heavy Precipitation and Ecological Water Replenishment B. Shi et al. 10.2139/ssrn.4106361
27. Intelligent approach to predict future groundwater level based on artificial neural networks (ANN) M. Derbela & I. Nouiri 10.1007/s41207-020-00185-9
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29. Improving the resolution of GRACE-based water storage estimates based on machine learning downscaling schemes W. Yin et al. 10.1016/j.jhydrol.2022.128447
30. A causal physics-informed deep learning formulation for groundwater flow modeling and climate change effect analysis A. Adombi et al. 10.1016/j.jhydrol.2024.131370
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32. Predicting Groundwater Levels in Ogallala Aquifer Wells Using Hierarchical Cluster Analysis and Artificial Neural Networks L. Oliveira et al. 10.1061/JHYEFF.HEENG-5840
33. Groundwater Level Modeling Using Multiobjective Optimization with Hybrid Artificial Intelligence Methods F. Banadkooki & A. Haghighi 10.1007/s10666-023-09938-6
34. Predicting areas with ecotourism capability using artificial neural networks and linear discriminant analysis (case study: Arasbaran Protected Area, Iran) M. Talebi et al. 10.1007/s10668-020-00964-y
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37. Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland A. Amaranto et al. 10.2166/hydro.2018.002
38. Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization K. Khosravi et al. 10.5194/hess-22-4771-2018
39. Groundwater Level Prediction/Forecasting and Assessment of Uncertainty Using SGS and ARIMA Models: A Case Study in the Bauru Aquifer System (Brazil) E. Takafuji et al. 10.1007/s11053-018-9403-6
40. Groundwater Level Fluctuations in Coastal Aquifer: Using Artificial Neural Networks to Predict the Impacts of Climatical CMIP6 Scenarios A. Roshani & M. Hamidi 10.1007/s11269-022-03204-2
41. A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA A. Amaranto et al. 10.1029/2018WR024301
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43. Enhancement groundwater level prediction using hybrid ANN-HHO model: case study (Shabestar Plain in Iran) E. Mirzania et al. 10.1007/s12517-023-11584-x
44. Estimating uncertainty of mean water table depth in the contiguous United States using highly parameterized linear inverse method J. Jiao 10.1016/j.jhydrol.2024.131380
45. GCMs simulation-based assessment for the response of the Mediterranean Gaza coastal aquifer to climate-induced changes H. Al-Najjar et al. 10.2166/wcc.2022.339
46. Analysis and prediction of the changes in groundwater resources under heavy precipitation and ecological water replenishment B. Shi et al. 10.2166/wcc.2023.348
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48. Impact of a new geological modelling method on the enhancement of the CO2 storage assessment of E sequence of Nam Vang field, offshore Vietnam H. Vo Thanh et al. 10.1080/15567036.2019.1604865
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51. A review of the artificial intelligence methods in groundwater level modeling T. Rajaee et al. 10.1016/j.jhydrol.2018.12.037
52. Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India J. Mohapatra et al. 10.1016/j.scitotenv.2021.147319
53. Groundwater Recharge Prediction Using Linear Regression, Multi-Layer Perception Network, and Deep Learning X. Huang et al. 10.3390/w11091879
54. Formulating a strategy to combine artificial intelligence models using Bayesian model averaging to study a distressed aquifer with sparse data availability M. Moazamnia et al. 10.1016/j.jhydrol.2019.02.011
55. Improving the spatial resolution of GRACE-based groundwater storage estimates using a machine learning algorithm and hydrological model W. Yin et al. 10.1007/s10040-021-02447-4
56. A novel model for prediction of uniaxial compressive strength of rocks X. Xue 10.5802/crmeca.109
57. Development and Application of an Integrated Hydrological Model for Singapore Freshwater Swamp Forest Y. Sun et al. 10.1016/j.proeng.2016.07.589