73 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. 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
4. 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
5. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
6. GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present J. Yin et al. 10.5194/essd-15-5597-2023
7. Integrated learning model for water intake capacity of Tyrolean weirs under supercritical flow G. Shen et al. 10.2166/hydro.2024.192
8. 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
9. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
10. 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
11. Global streamflow modelling using process-informed machine learning M. Magni et al. 10.2166/hydro.2023.217
12. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. 10.1029/2021WR029583
13. 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
14. Using supervised machine learning for regional hydrological hazard estimation in metropolitan France Q. Ding & P. Arnaud 10.1016/j.ejrh.2024.101872
15. Multifactorial Principal‐Monotonicity Inference for Macro‐Scale Distributed Hydrologic Modeling G. Cheng et al. 10.1029/2021WR031370
16. 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
17. A spatial weather generator based on conditional deep convolution generative adversarial nets (cDCGAN) J. Sha et al. 10.1007/s00382-023-06971-9
18. 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
19. Water surface profile prediction in compound channels with vegetated floodplains M. Mohseni & A. Naseri 10.1680/jwama.21.00005
20. Streamflow prediction using machine learning models in selected rivers of Southern India R. Sharma et al. 10.1080/15715124.2023.2196635
21. Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. 10.3390/su16041376
22. 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
23. 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
24. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. 10.1016/j.jhydrol.2023.130038
25. 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
26. 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
27. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
28. 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
29. 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
30. 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
31. Trends and variability of rainfall characteristics influencing annual streamflow: A case study of southeast Australia G. Fu et al. 10.1002/joc.7923
32. 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
33. 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
34. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
35. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
36. 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
37. Comparison of Machine Learning Models to Predict Lake Area in an Arid Area D. Wang et al. 10.3390/rs15174153
38. 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
39. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
40. Physics-informed identification of PDEs with LASSO regression, examples of groundwater-related equations Y. Zhan et al. 10.1016/j.jhydrol.2024.131504
41. 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
42. A parsimonious methodological framework for short-term forecasting of groundwater levels A. Collados-Lara et al. 10.1016/j.scitotenv.2023.163328
43. Exploring climate-change impacts on streamflow and hydropower potential: insights from CMIP6 multi-GCM analysis N. Chanda et al. 10.2166/wcc.2024.150
44. Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting K. Lin et al. 10.1016/j.scitotenv.2023.164494
45. 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
46. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
47. Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka N. Rathnayake et al. 10.1109/ACCESS.2023.3238717
48. 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
49. A hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling B. Yifru et al. 10.2166/nh.2024.016
50. 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
51. 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
52. 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
53. 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
54. 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
55. Real-time water quality detection based on fluctuation feature analysis with the LSTM model L. Wang et al. 10.2166/hydro.2023.127
56. 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
57. 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
58. Global prediction of extreme floods in ungauged watersheds G. Nearing et al. 10.1038/s41586-024-07145-1
59. 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
60. Smooth Spatial Modeling of Extreme Mediterranean Precipitation H. Hammami et al. 10.3390/w14223782
61. 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
62. Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez 10.1016/j.advwatres.2024.104796
63. 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
64. 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
65. Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization H. Mao et al. 10.3390/w16213030
66. 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
67. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
68. Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model J. Wu et al. 10.1029/2023WR035676
69. A Deep Learning Approach Based on Physical Constraints for Predicting Soil Moisture in Unsaturated Zones Y. Wang et al. 10.1029/2023WR035194
70. A workflow to address pitfalls and challenges in applying machine learning models to hydrology A. Gharib & E. Davies 10.1016/j.advwatres.2021.103920
71. 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
72. 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
73. Genetic programming for hydrological applications: to model or to forecast that is the question H. Herath et al. 10.2166/hydro.2021.179