115 citations as recorded by crossref.

1. Optimized Intelligent Auto-Regressive Neural Network Model (ARNN) for Prediction of Non-Linear Exogenous Signals L. Farhi & A. Yasir 10.1007/s11277-021-09399-8
2. MONTHLY STREAM FLOWS ESTIMATION IN THE KARASU RIVER OF EUPHRATES BASIN WITH ARTIFICIAL NEURAL NETWORKS APPROACH O. KATİPOĞLU 10.21923/jesd.982868
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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
9. Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones A. Tegegne & G. Angiulli 10.1155/2022/6372089
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12. Predicting indoor particle concentration in mechanically ventilated classrooms using neural networks: Model development and generalization ability analysis J. Ren et al. 10.1016/j.buildenv.2023.110404
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15. 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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22. Simulation of Quantity and Quality of Saq Aquifer Using Artificial Intelligence and Hydraulic Models A. Ghumman et al. 10.1155/2022/5910989
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26. 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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28. 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
29. Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al. 10.1111/1752-1688.13170
30. 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
31. 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
32. A Novel Fused NARX-Driven Digital Twin Model for Aeroengine Gas Path Parameter Prediction C. Xu et al. 10.1109/TII.2023.3345462
33. 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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36. 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
37. 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
38. 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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41. Data‐Driven Estimation of Groundwater Level Time‐Series at Unmonitored Sites Using Comparative Regional Analysis E. Haaf et al. 10.1029/2022WR033470
42. 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
43. 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
44. 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
45. 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
46. Sustainable groundwater management using stacked LSTM with deep neural network E. Alabdulkreem et al. 10.1016/j.uclim.2023.101469
47. 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
48. 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
49. Graph neural network for groundwater level forecasting T. Bai & P. Tahmasebi 10.1016/j.jhydrol.2022.128792
50. Groundwater level forecasting with machine learning models: A review K. Boo et al. 10.1016/j.watres.2024.121249
51. 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
52. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
53. Drug Design and Disease Diagnosis: The Potential of Deep Learning Models in Biology S. Sreeraman et al. 10.2174/1574893618666230227105703
54. Machine Learning for Surrogate Groundwater Modelling of a Small Carbonate Island K. Payne et al. 10.3390/hydrology10010002
55. A transformer neural network for predicting near‐surface temperature E. Alerskans et al. 10.1002/met.2098
56. 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
57. Deep learning framework with Bayesian data imputation for modelling and forecasting groundwater levels E. Chen et al. 10.1016/j.envsoft.2024.106072
58. 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
59. Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning M. Alsarayreh et al. 10.3390/su14020870
60. 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
61. A Novel Hybrid Algorithms for Groundwater Level Prediction M. Saroughi et al. 10.1007/s40996-023-01068-z
62. 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
63. 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
64. Deep learning shows declining groundwater levels in Germany until 2100 due to climate change A. Wunsch et al. 10.1038/s41467-022-28770-2
65. Comparative Analysis of Time Series Neural Network Methods for Three-Way Catalyst Modeling Z. Yao et al. 10.1016/j.egyai.2024.100394
66. A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River L. Zhou & L. Kang 10.3390/w15081556
67. Groundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks A. Gharehbaghi et al. 10.1016/j.jhydrol.2022.128262
68. 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
69. 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
70. 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
71. Wavelet gated multiformer for groundwater time series forecasting V. Serravalle Reis Rodrigues et al. 10.1038/s41598-023-39688-0
72. 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
73. 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
74. Recurrent neural networks for rainfall-runoff modeling of small Amazon catchments L. de Mendonça et al. 10.1007/s40808-022-01626-w
75. 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
76. 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
77. 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
78. 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
79. Daily Groundwater Level Prediction and Uncertainty Using LSTM Coupled with PMI and Bootstrap Incorporating Teleconnection Patterns Information H. Chu et al. 10.3390/su141811598
80. A parsimonious methodological framework for short-term forecasting of groundwater levels A. Collados-Lara et al. 10.1016/j.scitotenv.2023.163328
81. 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
82. 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
83. Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations Y. Song et al. 10.1016/j.jhydrol.2024.131573
84. Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H. Gholizadeh et al. 10.1016/j.scitotenv.2023.165884
85. Modeling of daily groundwater level using deep learning neural networks M. OTHMAN 10.31127/tuje.1169908
86. A ConvLSTM Conjunction Model for Groundwater Level Forecasting in a Karst Aquifer Considering Connectivity Characteristics F. Guo et al. 10.3390/w13192759
87. A Machine learning framework to predict reverse flow and water level: A case study of Tonle Sap Lake K. Morovati et al. 10.1016/j.jhydrol.2021.127168
88. 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
89. Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand N. Sultana et al. 10.3390/en15093425
90. Groundwater-level prediction in Visakhapatnam district, Andhra Pradesh, India, using Bayesian Neural Networks R. Rajesh et al. 10.1007/s11600-023-01189-z
91. Are Neural Networks the Right Tool for Process Modeling and Control of Batch and Batch-like Processes? M. Rashid & P. Mhaskar 10.3390/pr11030686
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95. A parallel workflow framework using encoder-decoder LSTMs for uncertainty quantification in contaminant source identification in groundwater A. Anshuman & T. Eldho 10.1016/j.jhydrol.2023.129296
96. Predicting Changes in Spatiotemporal Groundwater Storage Through the Integration of Multi-Satellite Data and Deep Learning Models J. Seo & S. Lee 10.1109/ACCESS.2021.3130306
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98. Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran) M. Saroughi et al. 10.1016/j.heliyon.2024.e29006
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102. A deep CNN-LSTM model for predicting interface depth from gravity data over thrust and fold belts of North East India S. Maiti & R. Chiluvuru 10.1016/j.jseaes.2023.105881
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105. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
106. 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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109. 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
110. 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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113. Forecasting Future Groundwater Recharge from Rainfall Under Different Climate Change Scenarios Using Comparative Analysis of Deep Learning and Ensemble Learning Techniques D. Banerjee et al. 10.1007/s11269-024-03850-8
114. A Novel Approach to Uncertainty Quantification in Groundwater Table Modeling by Automated Predictive Deep Learning A. Abbaszadeh Shahri et al. 10.1007/s11053-022-10051-w
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