95 citations as recorded by crossref.

1. Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments P. Bhasme & U. Bhatia 10.1016/j.jhydrol.2023.130421
2. Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models V. Kumar et al. 10.3390/w15142572
3. Enhancing streamflow prediction in a mountainous watershed using a convolutional neural network with gridded data Z. Hajibagheri et al. 10.1007/s11356-024-35482-1
4. Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series A. Parsaie et al. 10.1016/j.jhydrol.2024.131041
5. Discerning the influence of climate variability modes, regional weather features and time series persistence on streamflow using Bayesian networks and multiple linear regression B. Bates & A. Dowdy 10.1002/joc.8368
6. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
7. GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present J. Yin et al. 10.5194/essd-15-5597-2023
8. Integrated learning model for water intake capacity of Tyrolean weirs under supercritical flow G. Shen et al. 10.2166/hydro.2024.192
9. Intercomparison of sediment transport curve and novel deep learning techniques in simulating sediment transport in the Wadi Mina Basin, Algeria M. Achite et al. 10.1007/s12665-024-12051-w
10. The Implementation of Multimodal Large Language Models for Hydrological Applications: A Comparative Study of GPT-4 Vision, Gemini, LLaVa, and Multimodal-GPT L. Kadiyala et al. 10.3390/hydrology11090148
11. Enhancing daily runoff prediction: A hybrid model combining GR6J-CemaNeige with wavelet-based gradient boosting technique B. Mohammadi et al. 10.1016/j.jhydrol.2025.133114
12. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
13. Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed R. Wang et al. 10.1111/jfr3.70022
14. Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test D. Vishwakarma et al. 10.1016/j.heliyon.2023.e16290
15. Global streamflow modelling using process-informed machine learning M. Magni et al. 10.2166/hydro.2023.217
16. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. 10.1029/2021WR029583
17. A framework on utilizing of publicly availability stream gauges datasets and deep learning in estimating monthly basin-scale runoff in ungauged regions M. Le et al. 10.1016/j.advwatres.2024.104694
18. Using supervised machine learning for regional hydrological hazard estimation in metropolitan France Q. Ding & P. Arnaud 10.1016/j.ejrh.2024.101872
19. A study on the runoff prediction mechanism of “water-soil-heat” in cold alpine regions with complex spatial distribution Q. Yu et al. 10.1016/j.scitotenv.2024.178059
20. Multifactorial Principal‐Monotonicity Inference for Macro‐Scale Distributed Hydrologic Modeling G. Cheng et al. 10.1029/2021WR031370
21. Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
22. A spatial weather generator based on conditional deep convolution generative adversarial nets (cDCGAN) J. Sha et al. 10.1007/s00382-023-06971-9
23. Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin Y. Wang et al. 10.3390/w15223928
24. Water surface profile prediction in compound channels with vegetated floodplains M. Mohseni & A. Naseri 10.1680/jwama.21.00005
25. Precipitation recycling impacts on runoff in arid regions of China and Mongolia: a machine learning approach R. Li et al. 10.1080/02626667.2025.2456211
26. Streamflow prediction using machine learning models in selected rivers of Southern India R. Sharma et al. 10.1080/15715124.2023.2196635
27. Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. 10.3390/su16041376
28. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
29. An interpretable machine learning model for predicting cavity water depth and cavity length based on XGBoost–SHAP T. Mo et al. 10.2166/hydro.2023.050
30. Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting M. Asif et al. 10.1007/s11269-025-04093-x
31. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. 10.1016/j.jhydrol.2023.130038
32. Prediction of climate change on surface water using NARX neural network model: a case study on Ghezel Ozan River, Northwest, Iran S. Mohammadi et al. 10.5004/dwt.2023.29802
33. Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin C. Yang et al. 10.1016/j.jhydrol.2023.129990
34. Integrating machine learning with process-based glacio-hydrological model for improving the performance of runoff simulation in cold regions B. Mohammadi et al. 10.1016/j.jhydrol.2025.132963
35. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
36. Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM J. Guo et al. 10.1016/j.jhydrol.2023.129969
37. Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand P. Tulla et al. 10.1007/s00704-024-04862-5
38. Synergistic approach for streamflow forecasting in a glacierized catchment of western Himalaya using earth observation and machine learning techniques J. Dharpure et al. 10.1007/s12145-024-01322-6
39. Enhancing runoff simulation by combining superflex with deep learning methods in China's Qinghai Lake Basin, Northeast Tibetan Plateau K. Liu et al. 10.1016/j.ejrh.2025.102331
40. Trends and variability of rainfall characteristics influencing annual streamflow: A case study of southeast Australia G. Fu et al. 10.1002/joc.7923
41. Realising smarter stormwater management: A review of the barriers and a roadmap for real world application C. Sweetapple et al. 10.1016/j.watres.2023.120505
42. Prediction and interpretation of pathogenic bacteria occurrence at a recreational beach using data-driven algorithms J. Jang et al. 10.1016/j.ecoinf.2023.102370
43. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
44. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
45. Review of machine learning and WEAP models for water allocation under climate change D. Hirko et al. 10.1007/s12145-025-01820-1
46. Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources J. Willard et al. 10.1017/eds.2024.14
47. Benchmarking data-driven rainfall-runoff modeling across 54 catchments in the Yellow River Basin: Overfitting, calibration length, dry frequency J. Jin et al. 10.1016/j.ejrh.2022.101119
48. Comparison of Machine Learning Models to Predict Lake Area in an Arid Area D. Wang et al. 10.3390/rs15174153
49. A Deep Learning Framework for Flash-Flood-Runoff Prediction: Integrating CNN-RNN with Neural Ordinary Differential Equations (ODEs) K. Alkaabi et al. 10.3390/w17091283
50. Enhancing interpretability of AI models in reservoir operation simulation: Exploring and mitigating principal inconsistencies through theory-guided multi-objective artificial neural networks A. Mahmoud et al. 10.1016/j.jhydrol.2024.131618
51. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
52. Physics-informed identification of PDEs with LASSO regression, examples of groundwater-related equations Y. Zhan et al. 10.1016/j.jhydrol.2024.131504
53. Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data D. Singh et al. 10.5194/hess-27-1047-2023
54. A parsimonious methodological framework for short-term forecasting of groundwater levels A. Collados-Lara et al. 10.1016/j.scitotenv.2023.163328
55. Exploring climate-change impacts on streamflow and hydropower potential: insights from CMIP6 multi-GCM analysis N. Chanda et al. 10.2166/wcc.2024.150
56. A review of current trends, challenges, and future perspectives in machine learning applications to water resources in Nepal S. Chaulagain et al. 10.1016/j.hazadv.2025.100678
57. Subgrid informed neural networks for high-resolution flood mapping H. Herath et al. 10.1016/j.jhydrol.2025.133329
58. Multi-station Joint Runoff Forecasting Using Graph Neural Network Coupled with Spatial Connectivity of Hydrological Stations Q. Wang & S. Zhu 10.1007/s11004-024-10167-0
59. Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting K. Lin et al. 10.1016/j.scitotenv.2023.164494
60. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al. 10.3390/w16131904
61. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
62. Editorial: Spatiotemporal modelling and assessment of water-related multi-hazards P. Ganguli et al. 10.3389/frwa.2025.1579106
63. Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka N. Rathnayake et al. 10.1109/ACCESS.2023.3238717
64. Concentration Field Estimation on Effluent Mixing and Transport With a Parameter‐Based Field Reconstruction Convolutional Neural Network and Random Forests X. Yan et al. 10.1029/2022WR033375
65. A hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling B. Yifru et al. 10.2166/nh.2024.016
66. Simulating runoff changes and evaluating under climate change using CMIP6 data and the optimal SWAT model: a case study S. Wang et al. 10.1038/s41598-024-74269-9
67. Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability A. El Bilali et al. 10.1007/s11356-024-34245-2
68. Differences in extremes and uncertainties in future runoff simulations using SWAT and LSTM for SSP scenarios Y. Song et al. 10.1016/j.scitotenv.2022.156162
69. A new stable and interpretable flood forecasting model combining multi-head attention mechanism and multiple linear regression Y. Wang et al. 10.2166/hydro.2023.160
70. Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review A. Chew et al. 10.1007/s11831-024-10145-z
71. Real-time water quality detection based on fluctuation feature analysis with the LSTM model L. Wang et al. 10.2166/hydro.2023.127
72. Physical information-fused deep learning model ensembled with a subregion-specific sampling method for predicting flood dynamics C. Li et al. 10.1016/j.jhydrol.2023.129465
73. On the future of hydroecological models of everywhere K. Beven 10.1016/j.envsoft.2025.106431
74. Using a two-step downscaling method to assess the impact of climate change on total nitrogen load in a small basin X. Li et al. 10.1016/j.jhydrol.2023.130510
75. Global prediction of extreme floods in ungauged watersheds G. Nearing et al. 10.1038/s41586-024-07145-1
76. Multi-source meteorological data assessment on daily runoff simulation in the upper reaches of the Hei River, Northwest China H. Xue et al. 10.1016/j.ejrh.2024.102100
77. A comprehensive study of various regressions and deep learning approaches for the prediction of friction factor in mobile bed channels A. Bassi et al. 10.2166/hydro.2023.246
78. Exploring the influence of training sampling strategies on time-series deep learning model in hydrology S. Yoon & K. Ahn 10.1016/j.jhydrol.2025.132774
79. Smooth Spatial Modeling of Extreme Mediterranean Precipitation H. Hammami et al. 10.3390/w14223782
80. Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends A. Saha & S. Chandra Pal 10.1016/j.jhydrol.2024.130907
81. Harnessing Novel Data‐Driven Techniques for Precise Rainfall–Runoff Modeling S. Sammen et al. 10.1111/jfr3.70013
82. Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez 10.1016/j.advwatres.2024.104796
83. A hybrid model enhancing streamflow forecasts in paddy land use-dominated catchments with numerical weather prediction model-based meteorological forcings A. Mohanty et al. 10.1016/j.jhydrol.2024.131225
84. Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability L. Trotter et al. 10.5194/gmd-15-6359-2022
85. Streamflow regime-based classification and hydrologic similarity analysis of catchment behavior using differentiable modeling with multiphysics outputs Y. Hu et al. 10.1016/j.jhydrol.2025.132766
86. Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization H. Mao et al. 10.3390/w16213030
87. Development of a physics-informed data-driven model for gaining insights into hydrological processes in irrigated watersheds K. Li et al. 10.1016/j.jhydrol.2022.128323
88. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
89. Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model J. Wu et al. 10.1029/2023WR035676
90. A Deep Learning Approach Based on Physical Constraints for Predicting Soil Moisture in Unsaturated Zones Y. Wang et al. 10.1029/2023WR035194
91. Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation D. Chandran & N. Chithra 10.1007/s11269-024-04029-x
92. A workflow to address pitfalls and challenges in applying machine learning models to hydrology A. Gharib & E. Davies 10.1016/j.advwatres.2021.103920
93. Improving the performance of random forest for estimating monthly reservoir inflow via complete ensemble empirical mode decomposition and wavelet analysis F. Ahmadi et al. 10.1007/s00477-021-02159-x
94. AUGMENTATION OF THE URBAN GREEN INFRASTRUCTURE USING STORMWATER SURFACE RUNOFF AS A RESOURCE IN THE NICE EXPRESSWAY, KARNATAKA, INDIA M. Shreewatsav & V. Sheriff 10.3846/jeelm.2022.16394
95. Genetic programming for hydrological applications: to model or to forecast that is the question H. Herath et al. 10.2166/hydro.2021.179