35 citations as recorded by crossref.

1. Daily river flow simulation using ensemble disjoint aggregating M5-Prime model K. Khosravi et al. 10.1016/j.heliyon.2024.e37965
2. Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6 H. Anwar et al. 10.1038/s41598-024-63989-7
3. 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
4. Performance evaluation of various hydrological models with respect to hydrological responses under climate change scenario: a review Y. Bihon et al. 10.1080/23311916.2024.2360007
5. 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
6. Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models V. Kumar et al. 10.3390/w15142572
7. Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning F. Ghobadi et al. 10.1016/j.jhydrol.2024.130772
8. Multi-scenario simulation of runoff and nutrient loads in a rapidly urbanizing watershed during China's Dual Carbon periods J. Wu et al. 10.1016/j.envres.2023.117272
9. Futuristic Streamflow Prediction Based on CMIP6 Scenarios Using Machine Learning Models B. Ullah et al. 10.1007/s11269-023-03645-3
10. Divergent path: isolating land use and climate change impact on river runoff S. Mahmood et al. 10.3389/fenvs.2024.1338512
11. Coupling SWAT and LSTM for Improving Daily Streamflow Simulation in a Humid and Semi-humid River Basin Z. Mei et al. 10.1007/s11269-024-03975-w
12. Assessing climate change impacts in the Cauvery Basin using evapotranspiration projections and its implications on water management A. J & A. E 10.1007/s00704-024-04998-4
13. A blueprint for coupling a hydrological model with fine- and coarse-scale atmospheric regional climate change models for probabilistic streamflow projections C. Rajulapati et al. 10.1016/j.jhydrol.2024.132080
14. An analysis framework for stationary and nonstationary sediment load frequency in a changing climate X. Yang et al. 10.1007/s00477-024-02763-7
15. GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present J. Yin et al. 10.5194/essd-15-5597-2023
16. Runoff Forecasting of Machine Learning Model Based on Selective Ensemble S. Liu et al. 10.1007/s11269-023-03566-1
17. Modeling streamflow response under changing environment using a modified SWAT model with enhanced representation of CO2 effects B. Li et al. 10.1016/j.ejrh.2023.101547
18. Exploring climate-change impacts on streamflow and hydropower potential: insights from CMIP6 multi-GCM analysis N. Chanda et al. 10.2166/wcc.2024.150
19. Inconsistent Monthly Runoff Prediction Models Using Mutation Tests and Machine Learning M. Ren et al. 10.1007/s11269-024-03911-y
20. Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change V. Kartal 10.1002/hyp.15204
21. Deep learning algorithms and their fuzzy extensions for streamflow prediction in climate change framework R. Vogeti et al. 10.2166/wcc.2024.594
22. Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data R. Adnan et al. 10.1007/s00704-023-04624-9
23. Assessment of the impact of climate change on streamflow of Ganjiang River catchment via LSTM-based models C. Deng et al. 10.1016/j.ejrh.2024.101716
24. Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches P. Chauhan et al. 10.1016/j.aiig.2024.100069
25. Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River Basin Q. Nguyen et al. 10.2166/wcc.2023.313
26. Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios B. Mishra et al. 10.2166/wst.2024.011
27. Comprehensive assessment of cascading dams-induced hydrological alterations in the lancang-mekong river using machine learning technique W. Achini Ishankha et al. 10.1016/j.jenvman.2024.123082
28. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
29. Response of the Northwest Indian Ocean purpleback flying squid (Sthenoteuthis oualaniensis) fishing grounds to marine environmental changes and its prediction model construction based on multi-models and multi-spatial and temporal scales H. Han et al. 10.1016/j.ecolind.2023.110809
30. Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective M. Khan et al. 10.2166/wst.2023.340
31. Comparative Analysis of Multiple Hydrological Models in Assessing Climate Change Impacts on the Mille Watershed, Awash Basin, Ethiopia A. Nigussie et al. 10.1155/2024/1439635
32. Improved monthly streamflow prediction using integrated multivariate adaptive regression spline with K-means clustering: implementation of reanalyzed remote sensing data O. Kisi et al. 10.1007/s00477-024-02692-5
33. Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years U. Singh et al. 10.1016/j.inffus.2023.101807
34. Performance evaluation of different gridded precipitation and CMIP6 model products with gauge observations for assessing rainfall variability under the historical and future climate change scenario over a semi-arid catchment, India D. Chatterjee et al. 10.1016/j.pce.2023.103433
35. A novel framework for peak flow estimation in the himalayan river basin by integrating SWAT model with machine learning based approach S. Raaj et al. 10.1007/s12145-023-01163-9