49 citations as recorded by crossref.

1. Enhancing snow depth estimation with snow cover geometrical descriptors L. Ferrarin et al.
2. Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić
3. Annual Peak Runoff Forecasting Using Two-Stage Input Variable Selection-Aided k-Nearest-Neighbors Ensemble W. Sun et al.
4. Deciphering the daily spatiotemporal dynamics and mechanisms of floods in the Tarim Basin desert region A. Tursun et al.
5. Streamflow Estimation in a Mediterranean Watershed Using Neural Network Models: A Detailed Description of the Implementation and Optimization A. Oliveira et al.
6. Enhancing hydrological simulation and climate change impact assessment for the Poyang Lake Region, China: A novel hybrid SWAT-GCN-BiLSTM framework X. Zheng et al.
7. Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al.
8. Enhancing streamflow prediction in a mountainous watershed using a convolutional neural network with gridded data Z. Hajibagheri et al.
9. Karst spring discharge modeling based on deep learning using spatially distributed input data A. Wunsch et al.
10. Time Distributed Deep Learning Models for Purely Exogenous Forecasting: Application to Water Table Depth Predictions Using Weather Image Time Series M. Salis et al.
11. Enhancing streamflow predictions through basin-to-basin knowledge transfer: A novel strategy for deep learning models adaptation and generalization K. Nifa et al.
12. Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al.
13. LSTM-Based River Discharge Forecasting Using Spatially Gridded Input Data K. Rakhymbek et al.
14. Learning From Limited Temporal Data: Dynamically Sparse Historical Functional Linear Models With Applications to Earth Science J. Janssen et al.
15. A hybrid deep learning rainfall-runoff forecasting model Incorporating spatiotemporal information from multi-source data W. Liu et al.
16. A hybrid statistical-dynamical forecast of seasonal streamflow for a catchment in the Upper Columbia River basin in Canada T. Swift-LaPointe et al.
17. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al.
18. Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al.
19. Physics-encoded deep learning for integrated modeling of watershed hydrology and reservoir operations B. Yu et al.
20. Streamflow Simulation and Interpretability Analysis in Multi-Climatic Basins Using Physics-Based and Data-Driven Hybrid Models J. Chen et al.
21. Investigating the streamflow simulation capability of a new mass-conserving long short-term memory (MC-LSTM) model across the contiguous United States Y. Wang et al.
22. A comprehensive assessment of meteorological–hydrological indicator changes and their driving forces from a multi-temporal and spatial scale perspective H. Wang et al.
23. Runoff simulation based on granular computing by introducing terrain factors to construct climate characteristic index Y. Zhao et al.
24. A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River L. Zhou & L. Kang
25. Assessing temporal and spatial generalization of LSTMs for streamflow modeling in French watersheds with and without European training data M. Puche et al.
26. Identify suitable artificial groundwater recharge zones using hybrid deep learning models N. Khalillollahi et al.
27. An improved Hydrology-Informed attention LSTM(HIA-LSTM) model for runoff simulation with seasonal snowmelt M. Ling et al.
28. Predicting Ili River streamflow change and identifying the major drivers with a novel hybrid model S. Liu et al.
29. A Robust Calibration and Evaluation Framework for Dynamic Catchment Characteristics in Hydrological Modeling T. Lan et al.
30. A parsimonious setup for streamflow forecasting using CNN-LSTM S. Pokharel & T. Roy
31. From unreliable observations to reliable forecasts: Enhancing Jakarta flood prediction using HEC-HMS-assisted LSTM modeling H. Kardhana et al.
32. Modeling the Streamflow Response to Heatwaves Across Glacierized Basins in Southwestern Canada S. Anderson & V. Radić
33. Does MC-LSTM model improve the reliability of streamflow prediction in human-influenced watersheds? G. Sahu et al.
34. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al.
35. Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo
36. Seasonal shifts in pan-Arctic Ocean downward radiation and key drivers Y. Liu et al.
37. An illustration of model agnostic explainability methods applied to environmental data C. Wikle et al.
38. Uncertainty and driving factor analysis of streamflow forecasting for closed-basin and interval-basin: Based on a probabilistic and interpretable deep learning model C. Xu et al.
39. A GNN routing module is all you need for LSTM Rainfall–Runoff models H. Mosaffa et al.
40. Hydrological insights from a comparative evaluation of LSTM and MC-LSTM networks in the Ebro River basin (Spain) I. González-Planet & C. Juez
41. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al.
42. Mapping Regional Meteorological Processes to Ozone Variability in the North China Plain and the Yangtze River Delta, China F. Hu et al.
43. A review of hybrid deep learning applications for streamflow forecasting K. Ng et al.
44. Karst aquifer discharge response to rainfall interpreted as anomalous transport D. Elhanati et al.
45. From Local to Regional: Deep Learning Models for Daily Water Discharge Forecasting in a Data-Scarce Basin and Engineered River N. Quang et al.
46. A hybrid Capsule-Transformer Network for daily runoff forecasting Z. Wu & H. Yan
47. A wavelet-assisted deep learning approach for simulating groundwater levels affected by low-frequency variability S. Chidepudi et al.
48. Unlocking the Potential of Artificial Intelligence for Sustainable Water Management Focusing Operational Applications D. Jayakumar et al.
49. Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information? S. Chidepudi et al.