28 citations as recorded by crossref.

1. 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. 10.5194/hess-29-841-2025
2. Analysis of Hydrological Memory Characteristics in Taiwan’s Catchments T. Fang et al. 10.3390/atmos16010019
3. Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production A. Sedai et al. 10.3390/forecast5010014
4. Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments K. Ma et al. 10.1016/j.jhydrol.2024.130841
5. A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
6. Assessing Hydrological Simulations with Machine Learning and Statistical Models E. Rozos 10.3390/hydrology10020049
7. Hierarchical Temporal-Scale Framework for Real-Time Streamflow Prediction in Reservoir-Regulated Basins J. Chang et al. 10.3390/su17094046
8. Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM Y. Hu et al. 10.1016/j.envsoft.2025.106527
9. Toward improved deep learning-based regionalized streamflow modeling : Exploiting the power of basin similarity Y. Xu et al. 10.1016/j.envsoft.2025.106374
10. Improved predictions in ungauged basins using flood marks L. Oudin et al. 10.1016/j.jhydrol.2025.133145
11. A Hybrid Model Combining the Cama-Flood Model and Deep Learning Methods for Streamflow Prediction M. Zhong et al. 10.1007/s11269-023-03583-0
12. Hybrid Approach for Early Warning of Mine Water: Energy Density-Based Identification of Water-Conducting Channels Combined With Water Inflow Prediction by SA-LSTM S. Yang et al. 10.1109/TGRS.2024.3384990
13. A Grid-Based Long Short-Term Memory Framework for Runoff Projection and Uncertainty in the Yellow River Source Area Under CMIP6 Climate Change H. Chu et al. 10.3390/w17050750
14. Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network M. Al Mehedi et al. 10.1016/j.jhydrol.2023.130076
15. Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al. 10.1007/s11442-024-2235-x
16. A hydrological knowledge-informed LSTM model for monthly streamflow reconstruction using distributed data: Application to typical rivers across the Tibetan plateau S. Hou et al. 10.1016/j.jhydrol.2024.132409
17. Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions? Y. Khoshkalam et al. 10.1016/j.jhydrol.2024.132540
18. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
19. Hydrological insights: Comparative analysis of gridded potential evapotranspiration products for hydrological simulations and drought assessment M. Abdallah et al. 10.1016/j.ejrh.2024.102113
20. Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI M. Park & H. Yang 10.3390/s24227205
21. Development of a One-Parameter New Exponential (ONE) Model for Simulating Rainfall-Runoff and Comparison with Data-Driven LSTM Model J. Lee & J. Noh 10.3390/w15061036
22. Pairing remote sensing and clustering in landscape hydrology for large-scale change identification: an application to the subarctic watershed of the George River (Nunavik, Canada) E. Sicaud et al. 10.5194/hess-28-65-2024
23. Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia) I. Leščešen et al. 10.3390/w17060907
24. Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare N. Alharbi et al. 10.3390/math11183942
25. Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al. 10.1016/j.ejrh.2024.101744
26. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés 10.1016/j.jhydrol.2024.132569
27. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan 10.5194/hess-29-785-2025
28. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352