74 citations as recorded by crossref.

1. Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool D. Hah et al.
2. Long-lead daily streamflow forecasting using Long Short-Term Memory model with different predictors J. Li et al.
3. Explainable Prediction of Power Generation for Cascaded Hydropower Systems Under Complex Spatiotemporal Dependencies Z. Li et al.
4. Exploration of dual-attention mechanism-based deep learning for multi-step-ahead flood probabilistic forecasting Z. Cui et al.
5. A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al.
6. 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.
7. Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method Z. Cui et al.
8. Forecasting short- and medium-term streamflow using stacked ensemble models and different meta-learners F. Granata & F. Di Nunno
9. Hybrid physically based and machine learning model to enhance high streamflow prediction S. López-Chacón et al.
10. A Residual Neural Network Integrated with a Hydrological Model for Global Flood Susceptibility Mapping Based on Remote Sensing Datasets J. Liu et al.
11. A forensic engineering framework for flood management of cascade reservoir systems M. Zeinali et al.
12. Ensemble Forecasts of Extreme Flood Events with Weather Forecasts, Land Surface Modeling and Deep Learning Y. Liu et al.
13. 长江流域洞庭湖区出入湖磷通量模拟及水质预测：机器学习与传统水文模型耦合方法 J. Liu et al.
14. MTV19ANet: A Multi-tier Visual Geometry Group 19 with Attention Network-Based Streamflow Prediction System S. A et al.
15. Innovative optimization-driven machine learning models for hourly streamflow forecasting P. Parisouj et al.
16. Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington J. Zhang et al.
17. Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al.
18. A spatiotemporal graph convolution-based model for daily runoff prediction in a river network with non-Euclidean topological structure L. Deng et al.
19. Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models S. Thalli Mani et al.
20. Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model W. Xu et al.
21. Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction S. López-Chacón et al.
22. Data‐driven artificial intelligence‐based streamflow forecasting, a review of methods, applications, and tools H. Jahanbani et al.
23. WITHDRAWN: Impact of Lag Window Configuration on Next-Day River Water Stage Forecasting Accuracy: Comparative Evaluation of Six Machine Learning and Deep Learning Algorithms M. Islam et al.
24. A new non-stationary standardised streamflow index using the climate indices and the optimal anthropogenic indices as covariates in the Wei River Basin, China M. Ren et al.
25. Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications D. Nguyen et al.
26. Impacts of inter-basin water diversion projects on the feedback loops of water supply–hydropower generation–environment conservation nexus J. Wang et al.
27. Evolution characterization and attribution analysis of hydrological drought in Ganjiang River based on hydrological model and deep learning coupling M. Li et al.
28. Evaluating the performance of CHIRPS and CPC precipitation data for streamflow forecasting using multiple linear regression and Long Short-Term Memory Neural Network model K. Hasan et al.
29. Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River N. Dong et al.
30. From Local to Regional: Deep Learning Models for Daily Water Discharge Forecasting in a Data-Scarce Basin and Engineered River N. Quang et al.
31. Improving the probabilistic drought prediction with soil moisture information under the ensemble streamflow prediction framework G. Kim et al.
32. Machine learning for postprocessing ensemble streamflow forecasts S. Sharma et al.
33. Application of a coupled mechanistic and data-driven model for water level prediction considering the temporal and spatial effects of runoff evolution in cascade hydropower stations Y. Zhang et al.
34. A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis
35. Failure thresholds and weak part identification in cascade reservoir system: A risk-based optimization framework H. Wang et al.
36. Application of LSTM coupled models in runoff simulation and prediction: a review S. Li et al.
37. Integrated Random Forest and APEX-MODFLOW model for predicting and mapping river salinity in the Animas Watershed, Colorado River Basin H. Han et al.
38. A Two‐Stage Framework for Bias and Reliability Tests of Ensemble Hydroclimatic Forecasts T. Zhao et al.
39. Integrating dynamic land surface processes and machine learning into a hydrological modeling framework: application to the Yellow River Basin Y. Wang et al.
40. Analysis of statistical post-processing methods for multi-model ensemble runoff forecasts in flood seasons B. Qu et al.
41. Precipitation Forecasting for Hydrologic Modeling in West-Central Florida using Seasonal Climate Outlooks M. Shrestha et al.
42. Effectiveness of nature-based solutions to reduce flooding in Quad Cities Metro Area (QCMA) using SWMM-HEC based flood model A. Wadhwa et al.
43. Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data T. Tu et al.
44. Machine learning-enhanced baseflow estimation from digital separation methods: insights from south-western Nigeria A. Awode et al.
45. Near-term forecasting of water reservoir storage capacities using long short-term memory E. Rohli et al.
46. Adapting subseasonal-to-seasonal (S2S) precipitation forecast at watersheds for hydrologic ensemble streamflow forecasting with a machine learning-based post-processing approach L. Zhang et al.
47. Improving cascade reservoir inflow forecasting and extracting insights by decomposing the physical process using a hybrid model J. Li et al.
48. Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling A. Kolling Neto et al.
49. Combining Satellite Imagery and a Deep Learning Algorithm to Retrieve the Water Levels of Small Reservoirs J. Wu et al.
50. Ensembles of machine learning and hydrodynamic numerical modeling for salinity simulations in a tidal estuary B. Zhu & P. Willems
51. Improving the predictive skills of hydrological models using a combinatorial optimization algorithm and artificial neural networks J. Farfán & L. Cea
52. Informer-Based Safety Risk Prediction of Heavy Metals in Rice in China P. Lu et al.
53. Spatiotemporal characteristics and forecasting of short-term meteorological drought in China Q. Zhang et al.
54. Impact assessment of cascade freshwater reservoir using the ecological security assessment (ESA) model across a four-year timescale J. Yin et al.
55. Enhancing streamflow prediction in a dam-regulated river by integrating mechanism and machine learning models W. Gao et al.
56. Application of Machine Learning in Water Resources Management: A Systematic Literature Review F. Ghobadi & D. Kang
57. Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets C. Mo et al.
58. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn
59. A physically guided and interpretable SWAT-BiLSTM framework with Bayesian optimization for bias correction in daily streamflow forecasting L. Jin et al.
60. Cascade Reservoirs: An Exploration of Spatial Runoff Storage Sites for Water Harvesting and Mitigation of Climate Change Impacts, Using an Integrated Approach of GIS and Hydrological Modeling A. Soomro et al.
61. Modeling sediment flow analysis for hydro-electric projects using deep neural networks S. Tomar et al.
62. Impact of impervious surface spatial morphologies on urban waterlogging: Insights from a cascade modeling chain at catchment scale X. Qin et al.
63. A long lead time forecast model applying an ensemble approach for managing the great Karun multi-reservoir system E. Mostaghimzadeh et al.
64. Improving Daily and Monthly River Discharge Forecasts using Geostatistical Ensemble Modeling F. Rezaei et al.
65. Deep Learning Prediction of Streamflow in Portugal R. Francisco & J. Matos
66. A review of hybrid deep learning applications for streamflow forecasting K. Ng et al.
67. Daily runoff forecasting using novel optimized machine learning methods P. Parisouj et al.
68. Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation F. Zhou et al.
69. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang
70. Daily Streamflow Forecasts Based on Cascade Long Short-Term Memory (LSTM) Model over the Yangtze River Basin J. Li & X. Yuan
71. Physics-aware machine learning revolutionizes scientific paradigm for process-based modeling in hydrology Q. Xu et al.
72. Climate warming outweighs vegetation greening in intensifying flash droughts over China M. Zhang et al.
73. Comparison of different ensemble precipitation forecast system evaluation, integration and hydrological applications Y. Tang et al.
74. Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction S. He et al.