131 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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29. 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. Forecasting of water quality parameters of Sandia station in Narmada basin, Central India, using AI techniques D. Tiwari et al. 10.2166/wcc.2024.520
32. Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al. 10.1111/1752-1688.13170
33. 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
34. 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
35. A Novel Fused NARX-Driven Digital Twin Model for Aeroengine Gas Path Parameter Prediction C. Xu et al. 10.1109/TII.2023.3345462
36. Runoff projection in the Tibetan Plateau using a long short-term memory network-based framework under various climate scenarios H. Chu et al. 10.1016/j.jhydrol.2024.130914
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40. 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
41. 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
42. 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. Application of Nonlinear Time Series and Machine Learning Algorithms for Forecasting Groundwater Flooding in a Lowland Karst Area B. Basu et al. 10.1029/2021WR029576
45. Data‐Driven Estimation of Groundwater Level Time‐Series at Unmonitored Sites Using Comparative Regional Analysis E. Haaf et al. 10.1029/2022WR033470
46. 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
47. 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
48. Shallow vs. Deep Learning Models for Groundwater Level Prediction: A Multi-Piezometer Data Integration Approach A. Yeganeh et al. 10.1007/s11270-024-07247-6
49. 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
50. 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
51. Sustainable groundwater management using stacked LSTM with deep neural network E. Alabdulkreem et al. 10.1016/j.uclim.2023.101469
52. 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
53. 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
54. Graph neural network for groundwater level forecasting T. Bai & P. Tahmasebi 10.1016/j.jhydrol.2022.128792
55. Groundwater level forecasting with machine learning models: A review K. Boo et al. 10.1016/j.watres.2024.121249
56. 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
57. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
58. Drug Design and Disease Diagnosis: The Potential of Deep Learning Models in Biology S. Sreeraman et al. 10.2174/1574893618666230227105703
59. Machine Learning for Surrogate Groundwater Modelling of a Small Carbonate Island K. Payne et al. 10.3390/hydrology10010002
60. A transformer neural network for predicting near‐surface temperature E. Alerskans et al. 10.1002/met.2098
61. 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
62. Deep learning framework with Bayesian data imputation for modelling and forecasting groundwater levels E. Chen et al. 10.1016/j.envsoft.2024.106072
63. 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
64. Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning M. Alsarayreh et al. 10.3390/su14020870
65. 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
66. A Novel Hybrid Algorithms for Groundwater Level Prediction M. Saroughi et al. 10.1007/s40996-023-01068-z
67. 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
68. 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
69. Deep learning shows declining groundwater levels in Germany until 2100 due to climate change A. Wunsch et al. 10.1038/s41467-022-28770-2
70. 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
71. A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River L. Zhou & L. Kang 10.3390/w15081556
72. Groundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks A. Gharehbaghi et al. 10.1016/j.jhydrol.2022.128262
73. 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
74. 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
75. 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
76. 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
77. Wavelet gated multiformer for groundwater time series forecasting V. Serravalle Reis Rodrigues et al. 10.1038/s41598-023-39688-0
78. 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
79. 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
80. Recurrent neural networks for rainfall-runoff modeling of small Amazon catchments L. de Mendonça et al. 10.1007/s40808-022-01626-w
81. Performance of long short-term memory networks in predicting athlete injury risk H. Tao et al. 10.3233/JCM-247563
82. 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
83. Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism M. Kordani et al. 10.1061/JHYEFF.HEENG-6262
84. 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
85. 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
86. 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
87. 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
88. 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
89. Daily Groundwater Level Prediction and Uncertainty Using LSTM Coupled with PMI and Bootstrap Incorporating Teleconnection Patterns Information H. Chu et al. 10.3390/su141811598
90. A parsimonious methodological framework for short-term forecasting of groundwater levels A. Collados-Lara et al. 10.1016/j.scitotenv.2023.163328
91. 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
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99. Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning Ž. Brkić & M. Kuhta 10.3390/su141610447
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125. A Hybrid Data Fusion Approach to AI-Assisted Indirect Centralized Integrated SINS/GNSS Navigation System During GNSS Outage E. Abdolkarimi et al. 10.1109/ACCESS.2021.3096422
126. 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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