128 citations as recorded by crossref.

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8. Predicting Groundwater Level Based on Machine Learning: A Case Study of the Hebei Plain Z. Wu et al. 10.3390/w15040823
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17. Prediction of the Irrigation Area Carrying Capacity in the Tarim River Basin under Climate Change Q. Liu et al. 10.3390/agriculture12050657
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28. Model-based prediction of water levels for the Great Lakes: a comparative analysis O. Kurt 10.1007/s12145-024-01341-3
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31. Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al. 10.1111/1752-1688.13170
32. Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions G. Cinkus et al. 10.5194/hess-27-1961-2023
33. 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
34. A Novel Fused NARX-Driven Digital Twin Model for Aeroengine Gas Path Parameter Prediction C. Xu et al. 10.1109/TII.2023.3345462
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39. Using Simple LSTM Models to Evaluate Effects of a River Restoration on Groundwater in Kushiro Wetland, Hokkaido, Japan T. Yamaguchi et al. 10.3390/w15061115
40. Application of the Data-Driven Method and Hydrochemistry Analysis to Predict Groundwater Level Change Induced by the Mining Activities: A Case Study of Yili Coalfield in Xinjiang, Norwest China A. Luo et al. 10.3390/w16111611
41. Multi-step-ahead prediction of river flow using NARX neural networks and deep learning LSTM G. Hayder et al. 10.2166/h2oj.2022.134
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44. Data‐Driven Estimation of Groundwater Level Time‐Series at Unmonitored Sites Using Comparative Regional Analysis E. Haaf et al. 10.1029/2022WR033470
45. A multi-layer nesting and integration approach for predicting groundwater levels in agriculturally intensive areas using data-driven models F. Zhu et al. 10.1016/j.jhydrol.2024.132038
46. Multiorder hydrologic Position for Europe — a Set of Features for Machine Learning and Analysis in Hydrology M. Nölscher et al. 10.1038/s41597-022-01787-4
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48. Future projection of water resources based on digitalisation and open data in a water-rich region: a case study of the city of Klagenfurt M. Oberascher et al. 10.2166/ws.2024.061
49. A SOM-LSTM combined model for groundwater level prediction in karst critical zone aquifers considering connectivity characteristics F. Guo et al. 10.1007/s12665-024-11567-5
50. Sustainable groundwater management using stacked LSTM with deep neural network E. Alabdulkreem et al. 10.1016/j.uclim.2023.101469
51. 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
52. Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States K. Khand & G. Senay 10.1016/j.mlwa.2024.100551
53. Graph neural network for groundwater level forecasting T. Bai & P. Tahmasebi 10.1016/j.jhydrol.2022.128792
54. Groundwater level forecasting with machine learning models: A review K. Boo et al. 10.1016/j.watres.2024.121249
55. Forecasting groundwater level of karst aquifer in a large mining area using partial mutual information and NARX hybrid model W. Zhang et al. 10.1016/j.envres.2022.113747
56. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
57. Drug Design and Disease Diagnosis: The Potential of Deep Learning Models in Biology S. Sreeraman et al. 10.2174/1574893618666230227105703
58. Machine Learning for Surrogate Groundwater Modelling of a Small Carbonate Island K. Payne et al. 10.3390/hydrology10010002
59. A transformer neural network for predicting near‐surface temperature E. Alerskans et al. 10.1002/met.2098
60. Development of a Joint Probabilistic Rainfall‐Runoff Model for High‐to‐Extreme Flow Projections Under Changing Climatic Conditions K. Li et al. 10.1029/2021WR031557
61. Deep learning framework with Bayesian data imputation for modelling and forecasting groundwater levels E. Chen et al. 10.1016/j.envsoft.2024.106072
62. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. 10.5194/hess-26-5493-2022
63. Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning M. Alsarayreh et al. 10.3390/su14020870
64. Hybrid support vector regression models with algorithm of innovative gunner for the simulation of groundwater level T. Roshni et al. 10.1007/s11600-022-00826-3
65. A Novel Hybrid Algorithms for Groundwater Level Prediction M. Saroughi et al. 10.1007/s40996-023-01068-z
66. Projected changes of groundwater levels in northeastern Romania according to climate scenarios for 2020–2100 L. Sfîcă et al. 10.1016/j.ejrh.2022.101108
67. 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
68. Deep learning shows declining groundwater levels in Germany until 2100 due to climate change A. Wunsch et al. 10.1038/s41467-022-28770-2
69. Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features M. Gomez et al. 10.5194/hess-28-4407-2024
70. A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River L. Zhou & L. Kang 10.3390/w15081556
71. Groundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks A. Gharehbaghi et al. 10.1016/j.jhydrol.2022.128262
72. A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al. 10.1016/j.asoc.2024.112352
73. Modified flood potential index (MFPI) for flood monitoring in terrestrial water storage depletion basin using GRACE estimates W. Jiang et al. 10.1016/j.jhydrol.2022.128765
74. A wavelet-assisted deep learning approach for simulating groundwater levels affected by low-frequency variability S. Chidepudi et al. 10.1016/j.scitotenv.2022.161035
75. Evaluation and optimization of the water diversion system of ecohydrological restoration megaproject of Tarim River, China, through wavelet analysis and a neural network Q. Liu et al. 10.1016/j.jhydrol.2022.127586
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77. A multi-scenario multi-model analysis of regional climate projections in a Central–Eastern European agricultural region: assessing shallow groundwater table responses using an aggregated vertical hydrological model L. Koncsos & G. Murányi 10.1007/s13201-023-02097-9
78. Towards understanding the influence of seasons on low-groundwater periods based on explainable machine learning A. Wunsch et al. 10.5194/hess-28-2167-2024
79. Recurrent neural networks for rainfall-runoff modeling of small Amazon catchments L. de Mendonça et al. 10.1007/s40808-022-01626-w
80. Performance of long short-term memory networks in predicting athlete injury risk H. Tao et al. 10.3233/JCM-247563
81. Sim-to-Real Transfer of Soft Robotic Navigation Strategies That Learns From the Virtual Eye-in-Hand Vision J. Lai et al. 10.1109/TII.2023.3291699
82. Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism M. Kordani et al. 10.1061/JHYEFF.HEENG-6262
83. Using Artificial Intelligence Algorithms to Estimate and Short-Term Forecast the Daily Reference Evapotranspiration with Limited Meteorological Variables S. Fang et al. 10.3390/agriculture14040510
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. 10.1016/j.hydres.2024.05.001
85. Multiscale groundwater level forecasts with multi-model ensemble approaches: Combining machine learning models using decision theories and bayesian model averaging D. Roy et al. 10.1016/j.gsd.2024.101347
86. Risk of groundwater depletion in Jaipur district, India: a prediction of groundwater for 2028 using artificial neural network M. Mondal 10.1007/s12210-024-01270-9
87. Entity aware sequence to sequence learning using LSTMs for estimation of groundwater contamination release history and transport parameters A. Anshuman & T. Eldho 10.1016/j.jhydrol.2022.127662
88. Daily Groundwater Level Prediction and Uncertainty Using LSTM Coupled with PMI and Bootstrap Incorporating Teleconnection Patterns Information H. Chu et al. 10.3390/su141811598
89. A parsimonious methodological framework for short-term forecasting of groundwater levels A. Collados-Lara et al. 10.1016/j.scitotenv.2023.163328
90. A Comparison of Ensemble and Deep Learning Algorithms to Model Groundwater Levels in a Data-Scarce Aquifer of Southern Africa Z. Gaffoor et al. 10.3390/hydrology9070125
91. Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence C. Zhan et al. 10.1016/j.watres.2024.121679
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118. The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco T. Bouramtane et al. 10.3389/frwa.2023.1241451
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123. Predicting the Spatial-Temporal Distribution of Human Brucellosis in Europe Based on Convolutional Long Short-Term Memory Network L. Shen et al. 10.1155/2022/7658880
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