101 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
3. High-frequency data significantly enhances the prediction ability of point and interval estimation X. Liu et al. 10.1016/j.scitotenv.2023.169289
4. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al. 10.3390/w14142211
5. Machine Learning-Based Water Management Strategies for Sustainable Groundwater Resources S. Sanu & M. Math 10.1007/s42979-024-02686-8
6. Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics Y. Wang et al. 10.1029/2021WR031033
7. Predicting Groundwater Level Based on Machine Learning: A Case Study of the Hebei Plain Z. Wu et al. 10.3390/w15040823
8. Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones A. Tegegne & G. Angiulli 10.1155/2022/6372089
9. The Applications of Soft Computing Methods for Seepage Modeling: A Review V. Nourani et al. 10.3390/w13233384
10. Hybrid model for daily runoff interval predictions based on Bayesian inference H. Xu et al. 10.1080/02626667.2022.2145201
11. 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
12. Sediment load prediction in Johor river: deep learning versus machine learning models S. Latif et al. 10.1007/s13201-023-01874-w
13. Prediction of Tail Water Level under the Influence of Backwater Effect Based on Deep Learning Models: A Case Study in the Xiangjiaba Hydropower Station S. Zhang et al. 10.3390/w15213854
14. Prediction of the Irrigation Area Carrying Capacity in the Tarim River Basin under Climate Change Q. Liu et al. 10.3390/agriculture12050657
15. An AHP based approach to forecast groundwater level at potential recharge zones of Uckermark District, Brandenburg, Germany A. Raihan et al. 10.1038/s41598-022-10403-9
16. Convolutional neural network and long short-term memory models for ice-jam predictions F. Madaeni et al. 10.5194/tc-16-1447-2022
17. A downscaling-calibrating framework for generating gridded daily precipitation estimates with a high spatial resolution J. Gu et al. 10.1016/j.jhydrol.2023.130371
18. Simulation of regional groundwater levels in arid regions using interpretable machine learning models Q. Liu et al. 10.1016/j.scitotenv.2022.154902
19. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
20. Simulation of Quantity and Quality of Saq Aquifer Using Artificial Intelligence and Hydraulic Models A. Ghumman et al. 10.1155/2022/5910989
21. Electric Vehicle Battery State of Charge Estimation With an Ensemble Algorithm Using Central Difference Kalman Filter (CDKF) and Non-Linear Autoregressive With Exogenous Input (NARX) A. Ogundana et al. 10.1109/ACCESS.2024.3371883
22. AutoML-GWL: Automated machine learning model for the prediction of groundwater level A. Singh et al. 10.1016/j.engappai.2023.107405
23. Karst spring discharge modeling based on deep learning using spatially distributed input data A. Wunsch et al. 10.5194/hess-26-2405-2022
24. Interannual and seasonal variations of permafrost thaw depth on the Qinghai-Tibetan Plateau: A comparative study using long short-term memory, convolutional neural networks, and random forest Q. Liu et al. 10.1016/j.scitotenv.2022.155886
25. 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
26. Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al. 10.1111/1752-1688.13170
27. 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
28. 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
29. A Novel Fused NARX-Driven Digital Twin Model for Aeroengine Gas Path Parameter Prediction C. Xu et al. 10.1109/TII.2023.3345462
30. 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
31. Machine learning for postprocessing ensemble streamflow forecasts S. Sharma et al. 10.2166/hydro.2022.114
32. Application of the improved dung beetle optimizer, muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area, China J. Cai et al. 10.1016/j.aosl.2024.100497
33. Groundwater level prediction using machine learning models: A comprehensive review H. Tao et al. 10.1016/j.neucom.2022.03.014
34. 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
35. Multi-step-ahead prediction of river flow using NARX neural networks and deep learning LSTM G. Hayder et al. 10.2166/h2oj.2022.134
36. 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
37. Data‐Driven Estimation of Groundwater Level Time‐Series at Unmonitored Sites Using Comparative Regional Analysis E. Haaf et al. 10.1029/2022WR033470
38. 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
39. 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
40. Sustainable groundwater management using stacked LSTM with deep neural network E. Alabdulkreem et al. 10.1016/j.uclim.2023.101469
41. 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
42. 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
43. Graph neural network for groundwater level forecasting T. Bai & P. Tahmasebi 10.1016/j.jhydrol.2022.128792
44. Groundwater level forecasting with machine learning models: A review K. Boo et al. 10.1016/j.watres.2024.121249
45. 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
46. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
47. Drug Design and Disease Diagnosis: The Potential of Deep Learning Models in Biology S. Sreeraman et al. 10.2174/1574893618666230227105703
48. Machine Learning for Surrogate Groundwater Modelling of a Small Carbonate Island K. Payne et al. 10.3390/hydrology10010002
49. A transformer neural network for predicting near‐surface temperature E. Alerskans et al. 10.1002/met.2098
50. 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
51. 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
52. Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning M. Alsarayreh et al. 10.3390/su14020870
53. 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
54. A Novel Hybrid Algorithms for Groundwater Level Prediction M. Saroughi et al. 10.1007/s40996-023-01068-z
55. 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
56. Deep learning shows declining groundwater levels in Germany until 2100 due to climate change A. Wunsch et al. 10.1038/s41467-022-28770-2
57. A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River L. Zhou & L. Kang 10.3390/w15081556
58. Groundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks A. Gharehbaghi et al. 10.1016/j.jhydrol.2022.128262
59. 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
60. 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
61. 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
62. Wavelet gated multiformer for groundwater time series forecasting V. Serravalle Reis Rodrigues et al. 10.1038/s41598-023-39688-0
63. 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
64. Recurrent neural networks for rainfall-runoff modeling of small Amazon catchments L. de Mendonça et al. 10.1007/s40808-022-01626-w
65. 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
66. 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
67. 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
68. Daily Groundwater Level Prediction and Uncertainty Using LSTM Coupled with PMI and Bootstrap Incorporating Teleconnection Patterns Information H. Chu et al. 10.3390/su141811598
69. A parsimonious methodological framework for short-term forecasting of groundwater levels A. Collados-Lara et al. 10.1016/j.scitotenv.2023.163328
70. 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
71. 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
72. Modeling of daily groundwater level using deep learning neural networks M. OTHMAN 10.31127/tuje.1169908
73. A ConvLSTM Conjunction Model for Groundwater Level Forecasting in a Karst Aquifer Considering Connectivity Characteristics F. Guo et al. 10.3390/w13192759
74. 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
75. 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
76. Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand N. Sultana et al. 10.3390/en15093425
77. Groundwater-level prediction in Visakhapatnam district, Andhra Pradesh, India, using Bayesian Neural Networks R. Rajesh et al. 10.1007/s11600-023-01189-z
78. Are Neural Networks the Right Tool for Process Modeling and Control of Batch and Batch-like Processes? M. Rashid & P. Mhaskar 10.3390/pr11030686
79. Prediction of groundwater level under the influence of groundwater exploitation using a data-driven method with the combination of time series analysis and long short-term memory: a case study of a coastal aquifer in Rizhao City, Northern China B. Guo et al. 10.3389/fenvs.2023.1253949
80. Advance prediction of coastal groundwater levels with temporal convolutional and long short-term memory networks X. Zhang et al. 10.5194/hess-27-83-2023
81. Artificial neural network-based model predictive control for optimal operating conditions in proton exchange membrane fuel cells Y. Cho et al. 10.1016/j.jclepro.2022.135049
82. 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
83. 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
84. Regional groundwater sequential forecasting using global and local LSTM models S. Patra et al. 10.1016/j.ejrh.2023.101442
85. 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
86. Use of meta-heuristic approach in the estimation of aquifer's response to climate change under shared socioeconomic pathways N. Zeydalinejad & R. Dehghani 10.1016/j.gsd.2022.100882
87. Deep transfer learning for groundwater flow in heterogeneous aquifers using a simple analytical model J. Zhang et al. 10.1016/j.jhydrol.2023.130293
88. Towards a better consideration of rainfall and hydrological spatial features by a deep neural network model to improve flash floods forecasting: case study on the Gardon basin, France B. Saint-Fleur et al. 10.1007/s40808-022-01650-w
89. 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
90. Groundwater level forecasting using ensemble coactive neuro-fuzzy inference system K. Boo et al. 10.1016/j.scitotenv.2023.168760
91. A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations D. Aboelyazeed et al. 10.5194/bg-20-2671-2023
92. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
93. 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
94. On the challenges of global entity-aware deep learning models for groundwater level prediction B. Heudorfer et al. 10.5194/hess-28-525-2024
95. Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy D. Feng et al. 10.1029/2022WR032404
96. 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
97. 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
98. Modeling of daily groundwater level using deep learning neural networks M. OTHMAN 10.31127/tuje.1169908
99. Prediction of leather footwear export using learning algorithms based on ANN model S. Nithiyanantha Vasagam et al. 10.1016/j.eswa.2023.121809
100. 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
101. 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