111 citations as recorded by crossref.

1. Flood Impact Assessment, LSTM-Based Forecasting, and Revenue Loss Analysis of the Melamchi Water Supply Project K. Katwal et al.
2. Predicting Délı̨nę ice road surface temperatures on Great Bear Lake in the Northwest Territories, Canada using long short-term memory networks and their relationship to ice melt processes I. Turnbull et al.
3. Climate teleconnection-driven stochastic simulation for future water-related risk management T. Lee & T. Ouarda
4. A GNN routing module is all you need for LSTM Rainfall–Runoff models H. Mosaffa et al.
5. Utilizing Deep Learning Models to Predict Streamflow H. Workneh & M. Jha
6. Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia) I. Leščešen et al.
7. Enhancing daily streamflow prediction: A comparative analysis of univariate LSTM and N-BEATS models with coupled SWAT-LSTM and SWAT-N-BEATS models incorporating influential SWAT features R. Priya & R. Manjula
8. Interpretable deep learning for sewer network water level forecasting in a Northern Chinese City: Implications for enhancing real-time assessment of system operational conditions Z. Yi et al.
9. Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al.
10. A Comparative Analysis of Advanced Machine Learning Techniques for River Streamflow Time-Series Forecasting A. Abdoulhalik & A. Ahmed
11. Enhancing mid- to long-term runoff simulation in human-impacted basins through coupled SWAT-LUT and LSTM modelin W. Zhou et al.
12. Forecasting the frequency and magnitude of hurricanes in the Yucatan Peninsula, Mexico, in the period from 2025 to 2034 using convolutional neural networks (CNNs), Long Short-Term Memory networks (LSTMs) and statistical models H. De Gracia et al.
13. Deep Learning-Based Correction of Decadal Predictions of the PDO and TAG Indices J. Dixit et al.
14. Deep learning-constrained projection of global fluvial floods and their socioeconomic implications under global warming X. Huang et al.
15. Enhancing flood prediction in the Lower Mekong River Basin by a scale-independent interpretable deep learning model Y. Qiu et al.
16. Accurate and interpretable prediction of chemical oxygen demand using explainable boosting algorithms with SHAP analysis K. Merabet et al.
17. Identifying trustworthiness challenges in deep learning models for continental-scale water quality prediction X. Xia et al.
18. Improving transparency in karst spring discharge and water quality forecasts using interpretable machine learning models in the Eastern Alps A. Pölz et al.
19. Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al.
20. Hydrological insights from a comparative evaluation of LSTM and MC-LSTM networks in the Ebro River basin (Spain) I. González-Planet & C. Juez
21. Coping with data scarcity in extreme flood forecasting: A deep generative modeling approach A. Sattari & H. Moradkhani
22. Improving Daily Streamflow Predictions over Large Watersheds: Introducing a Novel Enhanced Long Short-Term Memory (En-LSTM) Model P. Sharma et al.
23. A hybrid SWAT-LSTM model for streamflow simulation with SHAP-based interpretability: Application in the Wei River Basin, China W. Zhou et al.
24. Application of LSTM coupled models in runoff simulation and prediction: a review S. Li et al.
25. FEATURE SELECTION METHODS FOR LSTM-BASED RIVER WATER LEVEL AND DISCHARGE FORECASTING A. Alzhanov & A. Nugumanova
26. A Machine-Learning Framework for Modeling and Predicting Monthly Streamflow Time Series H. Dastour & Q. Hassan
27. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn
28. Spatial downscaling of GRACE terrestrial water storage anomalies for drought and flood potential assessment G. Yin et al.
29. Deep learning for cross-region streamflow and flood forecasting at a global scale B. Zhang et al.
30. Harnessing the strengths of machine learning and geostatistics to improve streamflow prediction in ungauged basins; the best of both worlds V. Grey et al.
31. Applications of machine learning to water resources management: A review of present status and future opportunities A. Ahmed et al.
32. 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.
33. 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.
34. Deep Learning Ensemble for Flood Probability Analysis F. Sseguya & K. Jun
35. Enhanced Time Series–Physics Model Approach for Dam Discharge Impacts on River Levels: Seomjin River, South Korea C. Jung et al.
36. The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting S. Hauswirth et al.
37. Non-Stationarity of Hydroclimatic Memory—Is Hydrological Memory Changing Under Climate Warming? M. Birylo
38. A hybrid statistical-dynamical forecast of seasonal streamflow for a catchment in the Upper Columbia River basin in Canada T. Swift-LaPointe et al.
39. 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.
40. Streamflow simulation at different temporal scales under rating curve uncertainty conditions using machine learning models N. Mena et al.
41. Characterizing the influence of remotely sensed wetland and lake water storage on discharge using LSTM models M. Vanderhoof et al.
42. Bias correction of Global Flood Awareness System (GloFAS) data for multi-station river flow prediction by ensemble modelling V. Nourani et al.
43. Decoding LSTM to Reveal Baseflow Contributions in Fractured and Sedimentary Mountain Basins: A Case Study in the Sangre de Cristo Mountains, Southwestern United States M. Rosati et al.
44. Deep Learning-Based River Flow Forecasting with MLPs: Comparative Exploratory Analysis Applied to the Tejo and the Mondego Rivers G. Jesus et al.
45. 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.
46. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al.
47. Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools K. Antwi-Agyakwa et al.
48. Integrated probabilistic forecasting framework for long-term reservoir outflow through dynamic coupling of meteorological–hydrological–engineering processes J. Tan et al.
49. Detecting sun glint in UAV RGB images at different times using a deep learning algorithm J. Chen et al.
50. DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States P. Ghaneei & H. Moradkhani
51. Comparative analysis of satellite and reanalysis data with ground‐based observations in Northern Ghana J. Katsekpor et al.
52. Streamflow forecasting using machine learning for flood management and mitigation in the White Volta basin of Ghana J. Katsekpor et al.
53. Integrating process-based and deep learning models for flood simulation in karst basins B. Li et al.
54. Daily river flow simulation using ensemble disjoint aggregating M5-Prime model K. Khosravi et al.
55. Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes V. Tran et al.
56. Effects of Multi-Step-Ahead Prediction Strategies on LSTM-Based Runoff Prediction J. Jiang et al.
57. 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.
58. Optimizing runoff simulation in three mid-high latitude catchments by integrating terrestrial ecosystem modelling, hybrid machine learning, and causal inference H. Zhou et al.
59. A Novel Spectral Decomposition Framework for TimesNet: Integrating DCT and Wavelet Transforms for Hydrological Forecasting K. Küllahcı et al.
60. KI-Modellierung für das Quellmanagement: Vorhersage von Karstquellschüttung und Wasserqualität mittels interpretierbarer Machine-Learning-Modelle A. Pölz et al.
61. Fusion of data-driven models with a knowledge-guided loss function for flood forecasting H. Malik et al.
62. Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs R. Qiu et al.
63. Reservoir computing for modeling and predicting stream chemistry P. Allsup et al.
64. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al.
65. Enhancing streamflow simulations with gated recurrent units deep learning models in the flood prone region with low-convergence streamflow data A. Karamvand et al.
66. Quantifying the Effects of National Water Model Freshwater Flux Predictions on Estuarine Hydrodynamic Forecasts N. Chin et al.
67. Investigation of the EWT–PSO–SVM Model for Runoff Forecasting in the Karst Area C. Mo et al.
68. Machine learning characterization of fuel temperature and moisture dynamics in wildland-urban interface for urban fire management J. Liu et al.
69. 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
70. Hybrid neutral network for daily streamflow projection on a sub-seasonal timescale E. Castro et al.
71. Does MC-LSTM model improve the reliability of streamflow prediction in human-influenced watersheds? G. Sahu et al.
72. Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review S. Nezhad et al.
73. Factors driving user behavior and value creation with text-to-image generative artificial intelligence (AI): A systems theory perspective C. Hung et al.
74. A lake water level prediction method based on data augmentation and Physics-Informed Neural Networks with imbalanced data L. Lu et al.
75. Impacts of spatially inconsistent permafrost degradation on streamflow in the Lena River Basin Z. Xue et al.
76. Hydro–Meteorological Coupled Runoff Forecasting Using Multi-Model Precipitation Forecasts Z. Zhu et al.
77. 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.
78. Regional vs local LSTM models for short-term streamflow forecasting under operational constraints J. Saavedra-Garrido et al.
79. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn
80. Neuroforecasting of daily streamflows in the UK for short- and medium-term horizons: A novel insight F. Granata & F. Di Nunno
81. Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes P. Bosompemaa et al.
82. Runoff simulation and prediction of typical basins in the Jiziwan Region of the Yellow River Basin based on Long Short-Term Memory (LSTM) neural network J. Sun et al.
83. Lake Titicaca water level forecasting using data augmentation and recurrent neural networks A. Flores et al.
84. Advancing Machine Learning-Based Streamflow Prediction Through Event Greedy Selection, Asymmetric Loss Function, and Rainfall Forecasting Uncertainty S. Tofighi et al.
85. Adaptive physics-informed graph convolutional network for flow prediction in the downstream river network of the dongjiang river Y. Liu et al.
86. 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
87. 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.
88. 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
89. The representation of rivers in operational ocean forecasting systems: a review P. Matte et al.
90. Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al.
91. Incorporating empirical orthogonal function analysis into lumped hydrological models with grid data P. Jiang et al.
92. A review of hybrid deep learning applications for streamflow forecasting K. Ng et al.
93. Incorporating accumulated temperature and lagged precipitation effects in LSTM modeling improves the streamflow simulation in glacier dominated basins Y. Ao et al.
94. Multi-step ahead streamflow forecasting method using Embedding Multi-Layer Perceptron Y. Li & S. Yang
95. Hybrid modeling for daily streamflow forecasting: A study over the contiguous United States F. Zeng et al.
96. Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S. Y. Yang et al.
97. Operational low-flow forecasting using LSTMs J. Deng et al.
98. Effects of mass balance, energy balance, and storage-discharge constraints on LSTM for streamflow prediction S. Pokharel et al.
99. BuRNN (v1.0): a data-driven fire model S. Lampe et al.
100. Leveraging Immersive Digital Twins and AI-Driven Decision Support Systems for Sustainable Water Reserves Management: A Conceptual Framework T. Zhao et al.
101. Exploration of dual-attention mechanism-based deep learning for multi-step-ahead flood probabilistic forecasting Z. Cui et al.
102. Hybrid Deep Neural Architectures with Evolutionary Optimization and Explainable AI for Drought Susceptibility Assessment J. Liu et al.
103. RESEARCH ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN PRICE FORECASTING OF SOME COMMODITIES N. Thai Son
104. 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.
105. Estimation of seasonal ecological water demand in arid zone of Northwest China: An approach using the LSTM-random forest regression model C. Wang et al.
106. 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.
107. 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.
108. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi
109. Enhancing Prediction Accuracy and Data Handling for Environmental Applications in Innovative Modeling of Groundwater Level Fluctuations Based on the Tree Ensembles Technique D. Chi et al.
110. Streamflow prediction using Long Short-Term Memory networks: a case study at the Kratie Hydrological Station, Mekong River Basin N. Nguyen et al.
111. Artificial Intelligence in Hydrology: Advancements in Soil, Water Resource Management, and Sustainable Development S. Biazar et al.