48 citations as recorded by crossref.

1. Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al. 10.1016/j.jhydrol.2024.131986
2. A Comparative Analysis of Advanced Machine Learning Techniques for River Streamflow Time-Series Forecasting A. Abdoulhalik & A. Ahmed 10.3390/su16104005
3. A comparative evaluation of streamflow prediction using the SWAT and NNAR models in the Meenachil River Basin of Central Kerala, India M. Saranya & V. Vinish 10.2166/wst.2023.330
4. Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review S. Nezhad et al. 10.3390/jmse11112154
5. 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
6. Impacts of spatially inconsistent permafrost degradation on streamflow in the Lena River Basin Z. Xue et al. 10.1007/s11431-023-2757-2
7. A Machine-Learning Framework for Modeling and Predicting Monthly Streamflow Time Series H. Dastour & Q. Hassan 10.3390/hydrology10040095
8. Monthly Streamflow Prediction of the Source Region of the Yellow River Based on Long Short-Term Memory Considering Different Lagged Months H. Chu et al. 10.3390/w16040593
9. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn 10.1016/j.jenvman.2024.122114
10. Deep learning for cross-region streamflow and flood forecasting at a global scale B. Zhang et al. 10.1016/j.xinn.2024.100617
11. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn 10.1016/j.jhydrol.2024.130862
12. Neuroforecasting of daily streamflows in the UK for short- and medium-term horizons: A novel insight F. Granata & F. Di Nunno 10.1016/j.jhydrol.2023.129888
13. Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes P. Bosompemaa et al. 10.1016/j.envsoft.2024.106234
14. Applications of machine learning to water resources management: A review of present status and future opportunities A. Ahmed et al. 10.1016/j.jclepro.2024.140715
15. Basin-Scale Streamflow Projections for Greater Pamba River Basin, India Integrating GCM Ensemble Modelling and Flow Accumulation-Weighted LULC Overlay in Deep Learning Environment A. Geetha Raveendran Nair et al. 10.3390/su151914148
16. Combining Standard Artificial Intelligence Models, Pre-Processing Techniques, and Post-Processing Methods to Improve the Accuracy of Monthly Runoff Predictions in Karst-Area Watersheds C. Mo et al. 10.3390/app13010088
17. Deep Learning Ensemble for Flood Probability Analysis F. Sseguya & K. Jun 10.3390/w16213092
18. The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting S. Hauswirth et al. 10.5194/hess-27-501-2023
19. Performance of LSTM over SWAT in Rainfall-Runoff Modeling in a Small, Forested Watershed: A Case Study of Cork Brook, RI S. Shrestha & S. Pradhanang 10.3390/w15234194
20. Large-scale seasonal forecasts of river discharge by coupling local and global datasets with a stacked neural network: Case for the Loire River system M. Vu et al. 10.1016/j.scitotenv.2023.165494
21. High-performance prediction model combining minimum redundancy maximum relevance, circulant spectrum analysis, and machine learning methods for daily and peak streamflow L. Latifoğlu & E. Kaya 10.1007/s00704-023-04653-4
22. Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al. 10.1007/s00521-023-08922-1
23. Global hydrological reanalyses: The value of river discharge information for world‐wide downstream applications – The example of the Global Flood Awareness System GloFAS C. Prudhomme et al. 10.1002/met.2192
24. A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
25. 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
26. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
27. Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools K. Antwi-Agyakwa et al. 10.3390/w15030427
28. Operational low-flow forecasting using LSTMs J. Deng et al. 10.3389/frwa.2023.1332678
29. Effects of mass balance, energy balance, and storage-discharge constraints on LSTM for streamflow prediction S. Pokharel et al. 10.1016/j.envsoft.2023.105730
30. Detecting sun glint in UAV RGB images at different times using a deep learning algorithm J. Chen et al. 10.1016/j.ecoinf.2024.102660
31. Comparative analysis of satellite and reanalysis data with ground‐based observations in Northern Ghana J. Katsekpor et al. 10.1002/met.2226
32. Daily river flow simulation using ensemble disjoint aggregating M5-Prime model K. Khosravi et al. 10.1016/j.heliyon.2024.e37965
33. Exploration of dual-attention mechanism-based deep learning for multi-step-ahead flood probabilistic forecasting Z. Cui et al. 10.1016/j.jhydrol.2023.129688
34. Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes V. Tran et al. 10.1016/j.advwatres.2023.104569
35. Deep Learning Approach with LSTM for Daily Streamflow Prediction in a Semi-Arid Area: A Case Study of Oum Er-Rbia River Basin, Morocco K. Nifa et al. 10.3390/w15020262
36. RESEARCH ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN PRICE FORECASTING OF SOME COMMODITIES N. Thai Son 10.56824/vujs.2023a082
37. Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs R. Qiu et al. 10.1016/j.watres.2024.121314
38. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
39. Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration Y. Khoshkalam et al. 10.1016/j.jhydrol.2023.129682
40. Interpreting runoff forecasting of long short-term memory network: An investigation using the integrated gradient method on runoff data from the Han River Basin X. Jing et al. 10.1016/j.ejrh.2023.101549
41. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi 10.1016/j.jhydrol.2024.131835
42. Enhancing streamflow simulations with gated recurrent units deep learning models in the flood prone region with low-convergence streamflow data A. Karamvand et al. 10.1016/j.pce.2024.103737
43. Investigation of the EWT–PSO–SVM Model for Runoff Forecasting in the Karst Area C. Mo et al. 10.3390/app13095693
44. LSTM with spatiotemporal attention for IoT-based wireless sensor collected hydrological time-series forecasting J. Huang et al. 10.1007/s13042-023-01836-3
45. Improving short-term streamflow forecasting by flow mode clustering S. Liu et al. 10.1007/s00477-022-02367-z
46. 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
47. The future water vulnerability assessment of the Seoul metropolitan area using a hybrid framework composed of physically-based and deep-learning-based hydrologic models Y. Kim et al. 10.1007/s00477-022-02366-0
48. Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters H. Tao et al. 10.1016/j.envint.2023.107931