567 citations as recorded by crossref.

1. Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China Q. Zhu et al.
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7. Simulating runoff under changing climatic conditions: A comparison of the long short-term memory network with two conceptual hydrologic models P. Bai et al.
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9. Global or local modeling for XGBoost in geospatial studies upon simulated data and German COVID-19 infection forecasting X. Cheng & J. Ma
10. Improving the estimation of atmospheric water vapor pressure using interpretable long short-term memory networks B. Gao et al.
11. Machine learning-based hydrograph modeling with LSTM: A case study in the Jatigede Reservoir Catchment, Indonesia N. Andika et al.
12. Soil Water Accounting Network (SWAN): a novel neural network for modeling conceptual hydrological processes Z. Fang et al.
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15. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés
16. Computing extreme storm surges in Europe using neural networks T. Hermans et al.
17. Hydrological process knowledge in catchment modelling – Lessons and perspectives from 60 years development J. Refsgaard et al.
18. Comparing strategies for training LSTM models for street-scale urban flood prediction in Norfolk, Virginia J. Jeong et al.
19. Improved Streamflow Forecasting Through SWE-Augmented Spatio-Temporal Graph Neural Networks A. Akkala et al.
20. The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment D. Feng et al.
21. Daily station-level records of air temperature, snow depth, and ground temperature in the Northern Hemisphere V. Tran et al.
22. How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models? R. Hashemi et al.
23. Technical note: Investigating the potential for smartphone-based monitoring of evapotranspiration and land surface energy-balance partitioning A. Teuling et al.
24. How to deal w___ missing input data M. Gauch et al.
25. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al.
26. Identifying the Minimum Number of Flood Events for Reasonable Flood Peak Prediction of Ungauged Forested Catchments in South Korea H. Yang et al.
27. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al.
28. Coupling SWAT+ with LSTM for enhanced and interpretable streamflow estimation in arid and semi-arid watersheds, a case study of the Tagus Headwaters River Basin, Spain S. Asadi et al.
29. Improving multi-model ensemble streamflow forecasts by combining lumped, distributed and deep learning hydrological models W. Armstrong et al.
30. A Multilayer Perceptron Model for Stochastic Synthesis E. Rozos et al.
31. Flood prediction in Brazil: A review of trends, gaps and methodological perspectives Í. Souza & V. de Amorim Silva
32. 长江流域洞庭湖区出入湖磷通量模拟及水质预测：机器学习与传统水文模型耦合方法 J. Liu et al.
33. Hydrologic connectivity and dynamics of solute transport in a mountain stream: Insights from a long-term tracer test and multiscale transport modeling informed by machine learning P. Le et al.
34. The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset G. Ayzel & M. Heistermann
35. Simulating hydrologic pathway contributions in fluvial and karst settings: An evaluation of conceptual, physically-based, and deep learning modeling approaches A. Husic et al.
36. Rainfall and temperature-driven machine learning models for multi-station water level forecasting in the old Brahmaputra river, Bangladesh M. Islam et al.
37. Monte-Carlo-assisted endo-exo temporal transformer for high-confidence interval forecasting of daily runoff X. Hu et al.
38. Lowland transboundary river in a cold, semi-arid steppe: review of the Yesil River basin N. Ongdas et al.
39. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider
40. Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al.
41. Water Inflow Forecasting Based on Visual MODFLOW and GS-SARIMA-LSTM Methods Z. Yang et al.
42. Global big data laboratory experiment, integrated with kernel-based algorithm with an improved nonlinear ensemble for compressive strength modeling B. Salami et al.
43. A Machine Learning Strategy for Estimating Rainfall with Integrated Multisource Data N. Bhavana & T. Maheswari
44. Hydrologic multi-model ensemble predictions using variational Bayesian deep learning D. Li et al.
45. Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al.
46. The representation of rivers in operational ocean forecasting systems: a review P. Matte et al.
47. Designing pluvial flood-safe urban landscapes using differentiable surrogate flood models Z. Guo & J. Leitão
48. Distilling hydrological and land-surface model parameters from physio-geographical properties using text-generating AI M. Feigl et al.
49. Lake Heatwaves and the Driving Mechanism in US Lakes J. Xu et al.
50. From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction J. Liu et al.
51. Runoff Prediction Based on the Discharge of Pump Stations in an Urban Stream Using a Modified Multi-Layer Perceptron Combined with Meta-Heuristic Optimization W. Lee & E. Lee
52. Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al.
53. Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models S. Kouadri et al.
54. Determining the threshold value of surface runoff of South Koel River basin, Eastern India R. Paul & S. Mondal
55. Hybrid modeling for streamflow prediction in ungauged rivers with uncertainty quantification: a comparative analysis Y. Zeng et al.
56. A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al.
57. Retrievals and simulations of terrestrial water storage changes and runoff over the Tibetan Plateau: Challenges and opportunities X. Li et al.
58. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al.
59. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al.
60. VMDI-LSTM-ED: A novel enhanced decomposition ensemble model incorporating data integration for accurate non-stationary daily streamflow forecasting J. Liu et al.
61. Comparative analysis of rainfall-runoff simulation using a long short-term memory (LSTM) deep learning model and a conceptual hydrological model, HEC-HMS: a case study of the mountainous river basin of Nepal U. Marasini & M. Pokhrel
62. Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning H. Ji et al.
63. Short-term water quality prediction of reclaimed water plant effluent and key measurement sections based on a surrogate prediction model J. Feng et al.
64. Early warning of complex climate risk with integrated artificial intelligence M. Reichstein et al.
65. SFM_MF: A streamflow forecasting model based on model fusion for small-sample data in small and medium-sized rivers S. Zhang et al.
66. Towards an efficient streamflow forecasting method for event-scales in Ca River basin, Vietnam X. Le et al.
67. Streamflow Simulation and Interpretability Analysis in Multi-Climatic Basins Using Physics-Based and Data-Driven Hybrid Models J. Chen et al.
68. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al.
69. Robustness of Process-Based versus Data-Driven Modeling in Changing Climatic Conditions S. O et al.
70. Development of a Joint Probabilistic Rainfall‐Runoff Model for High‐to‐Extreme Flow Projections Under Changing Climatic Conditions K. Li et al.
71. Integrating geographic data and the SCS-CN method with LSTM networks for enhanced runoff forecasting in a complex mountain basin M. Merizalde et al.
72. Using Entity-Aware LSTM to Enhance Streamflow Predictions in Transboundary and Large Lake Basins Y. Park et al.
73. Analysis of runoff dynamics and flood formation: An investigation through an explainable deep learning framework T. Wang et al.
74. Spatiotemporal Modeling of Surface Water–Groundwater Interactions via Multi-Task Transformer-Based Learning H. Jing et al.
75. Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region K. Ekpetere et al.
76. Exploring the influence of training sampling strategies on time-series deep learning model in hydrology S. Yoon & K. Ahn
77. Multi-site deep learning for groundwater level prediction across global datasets: toward scalable applications under data scarcity A. Nolte et al.
78. Toward improved deep learning-based regionalized streamflow modeling : Exploiting the power of basin similarity Y. Xu et al.
79. Hydrological insights from a comparative evaluation of LSTM and MC-LSTM networks in the Ebro River basin (Spain) I. González-Planet & C. Juez
80. A Comparative Study of a Two-Dimensional Slope Hydrodynamic Model (TDSHM), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) Models for Runoff Prediction Y. Zhou et al.
81. A Hybrid XAJ-LSTM-TFM Model for Improved Runoff Simulation in the Poyang Lake Basin: Integrating Physical Processes with Temporal and Lag Feature Learning H. Jiang & C. Zhang
82. Basin-informed flood frequency analysis using deep learning exhibits consistent projected regional patterns over CONUS R. Lazin et al.
83. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al.
84. Runoff prediction in gauged and ungauged basins using Transformer-XAJ model H. Yin et al.
85. Technical note: How many models do we need to simulate hydrologic processes across large geographical domains? W. Knoben et al.
86. Sustainable water allocation under climate change: Deep learning approaches to predict drinking water shortages H. Moghaddam et al.
87. Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy W. Ouyang et al.
88. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al.
89. Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model Y. Yang & T. Chui
90. Low-Flow (7-Day, 10-Year) Classical Statistical and Improved Machine Learning Estimation Methodologies A. DelSanto et al.
91. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al.
92. Challenges of modelling climate change impacts on hydrology and water resources: AI is the game changer—a review C. Onyutha
93. On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation T. Wagener et al.
94. Benchmarking data-driven rainfall-runoff modeling across 54 catchments in the Yellow River Basin: Overfitting, calibration length, dry frequency J. Jin et al.
95. Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria J. Laimighofer et al.
96. Classification-enhanced LSTM model for predicting river water levels J. Luo et al.
97. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al.
98. Learning to filter: snow data assimilation using a Long Short-Term Memory network G. Blandini et al.
99. Improving the Accuracy of Dam Inflow Predictions Using a Long Short-Term Memory Network Coupled with Wavelet Transform and Predictor Selection T. Tran et al.
100. HieraBoost-Q: interpretable karst discharge prediction from multi-site electrical conductivity with SHAP-based mechanism insights Y. Zhu et al.
101. Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US G. Konapala et al.
102. Evaluating a process‐guided deep learning approach for predicting dissolved oxygen in streams J. Sadler et al.
103. In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models A. Abbas et al.
104. From Local to Regional: Deep Learning Models for Daily Water Discharge Forecasting in a Data-Scarce Basin and Engineered River N. Quang et al.
105. Explainable sequence-to-sequence GRU neural network for pollution forecasting S. Mirzavand Borujeni et al.
106. Mitigating flood risk at Shah Alam, Malaysia for sustainable development A. Leeonis et al.
107. Regionalisierung hydrologischer Modelle mit Function Space Optimization M. Feigl et al.
108. Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al.
109. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven
110. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al.
111. Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz
112. Impact of catchment and climate attributes on flood generating processes and their effect on flood statistics S. Fischer et al.
113. Regional stream temperature modeling in pristine Atlantic salmon rivers: A hybrid deterministic–Machine Learning approach I. Hani et al.
114. Deep learning for groundwater level simulation in unconfined aquifers across the contiguous United States: Analyzing simulations at multiple lead times and integrating groundwater signatures K. Boo et al.
115. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al.
116. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn
117. Spatiotemporal deep learning rainfall-runoff forecasting combined with remote sensing precipitation products in large scale basins S. Zhu et al.
118. Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models V. Tran et al.
119. Uncertainty in estimating the relative change of design floods under climate change: a stylized experiment with process-based, deep learning, and hybrid models S. Poudel et al.
120. AGFormer: Adaptive Spatiotemporal graph informed transformer for multi-reservoir inflow forecasting M. Fan et al.
121. Hybrid hydrological modeling for large alpine basins: a semi-distributed approach B. Li et al.
122. Hybrid Model Predictive Control for Sustainable Flood Management and Rainwater Resource Utilization in Open-Channel Irrigation Systems W. Hou et al.
123. How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al.
124. Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations Y. Zhang et al.
125. Hydrological Response of the Irrawaddy River Under Climate Change Based on CV-LSTM Model X. Luo et al.
126. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al.
127. When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al.
128. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al.
129. Developing an explainable deep learning module based on the LSTM framework for flood prediction Z. Zhang et al.
130. A Seasonal to Decadal Calibration of 1990–2100 Eastern Canadian Freshwater Discharge Simulations by Observations, Data Models, and Neural Networks R. Danielson et al.
131. Performance of different reanalysis rainfall datasets and hydrological models in gap-filling for Mpologoma catchment in Eastern Uganda M. Twinomuhangi et al.
132. Machine learning and predictive models for water management: a systematic review M. Torres González et al.
133. An Easy-to-Apply Machine Learning Framework for Hydrologic Evaluation of Ungauged Catchments B. Baishya & A. Sarma
134. Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand M. Wesselkamp et al.
135. Investigating the ability of deep learning on actual evapotranspiration estimation in the scarcely observed region X. Wang et al.
136. Operational low-flow forecasting using LSTMs J. Deng et al.
137. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al.
138. Streamflow prediction using Long Short-Term Memory networks: a case study at the Kratie Hydrological Station, Mekong River Basin N. Nguyen et al.
139. Extending river discharge forecast lead times with machine and deep learning for proactive hydrological hazard management in Bangladesh M. Islam et al.
140. Prediction of runoff in the upper reaches of the Hei River based on the LSTM model guided by physical mechanisms H. Xue et al.
141. Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al.
142. Panta Rhei: a decade of progress in research on change in hydrology and society H. Kreibich et al.
143. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al.
144. Classification and Zoning of Rivers by Their Water Regime: History, Methodology, and Perspectives N. Frolova et al.
145. Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach E. Koutsovili et al.
146. GRDC-Caravan: extending Caravan with data from the Global Runoff Data Centre C. Färber et al.
147. Frequency-Domain Convolutional Network With Historical Data Fusion Module for Regional Streamflow Prediction Y. Zhang et al.
148. Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments M. Farahani et al.
149. Incorporating artificial intelligence into the future of stormwater management M. Rahman et al.
150. Observation‐Constrained Projection of Global Flood Magnitudes With Anthropogenic Warming W. Liu et al.
151. LSTM-Based River Discharge Forecasting Using Spatially Gridded Input Data K. Rakhymbek et al.
152. Enhancing Hydrological Variable Prediction through Multitask LSTM Models Y. Yan et al.
153. Long‐Lead Drought Forecasting Across the Continental United States Using Burg Entropy Spectral Analysis With a Multiresolution Approach J. Han & V. Singh
154. Statistical learning of water budget outcomes accounting for target and feature uncertainty N. Martin & C. Yang
155. Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin S. Nimai et al.
156. Accelerating flood warnings by 10 hours: the power of river network topology in AI-enhanced flood forecasting H. Wang et al.
157. Parameter optimization method of hydrological model based on neural ordinary differential equations Q. Xiangzhao et al.
158. Vorhersage der Fließgewässertemperaturen in österreichischen Einzugsgebieten mittels Machine Learning-Verfahren M. Feigl et al.
159. Entity aware sequence to sequence learning using LSTMs for estimation of groundwater contamination release history and transport parameters A. Anshuman & T. Eldho
160. Physics-informed attention-based neural network for hyperbolic partial differential equations: application to the Buckley–Leverett problem R. Rodriguez-Torrado et al.
161. Groundwater level reconstruction using long-term climate reanalysis data and deep neural networks S. Chidepudi et al.
162. Cross-border impacts of cascade reservoirs on the temperature of the Lancang-Mekong river J. Wang et al.
163. Univariate approaches for forecasting of commercial octopus (Octopus vulgaris Cuvier, 1797) landings: A comprehensive analysis of traditional time series models, machine learning algorithms and a deep learning algorithm V. Sanz-Fernández et al.
164. A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al.
165. Review of speed optimisation for ship energy efficiency: methods, taxonomy and challenges A. Fan et al.
166. Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling K. Li et al.
167. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al.
168. Multi-resolution adaptive channel fusion transformer encoder LSTM for accurate streamflow prediction S. Apak et al.
169. Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment B. Sabzipour et al.
170. Predicting River Discharge in the Niger River Basin: A Deep Learning Approach S. Ogunjo et al.
171. FloodGNN-GRU: a spatio-temporal graph neural network for flood prediction A. Kazadi et al.
172. The role of previously glaciated landscapes in spatiotemporal variability of streamflow in snow-dominated watersheds: British Columbia, Canada X. Chen et al.
173. 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.
174. 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.
175. Deep learning modeling framework for multi-resolution streamflow generation F. Custodio Pereira do Carmo et al.
176. Physics-guided machine learning from simulated data with different physical parameters S. Chen et al.
177. Comment on Williams (2025): “Friends don't let friends use NSE or KGE for hydrologic model accuracy evaluation: A rant with data and suggestions for better practice” M. Clark et al.
178. Nonlinear hydrological time series modeling to forecast river level dynamics in the Rio Negro Uruguay basin J. Duque et al.
179. Bridging training–projection gaps in purely data-driven deep learning for runoff under climate change Y. Cen et al.
180. Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data K. Tayal et al.
181. Interpretable hierarchical Bayesian modeling of monthly streamflow for heterogeneous basins: A comparative study of two basins H. Wang et al.
182. Comparison of deep learning models and a typical process-based model in glacio-hydrology simulation X. Chen et al.
183. Rolling forecast of soil moisture under non-stationary conditions: a robust framework incorporating time-varying dynamics within and between variables C. Yu et al.
184. Data-driven rapid flood prediction mapping with catchment generalizability Z. Guo et al.
185. Earthquake Nowcasting with Deep Learning G. Fox et al.
186. Evaluation of shallow groundwater dynamics after water supplement in North China Plain based on attention-GRU model T. Nan et al.
187. Forecasting streamflow in Colorado by deep learning and NASA satellite remote sensing M. Valipour & A. Brown
188. Key drivers of physics-embedded machine learning performance for streamflow prediction A. Adombi & R. Chesnaux
189. FEATURE SELECTION METHODS FOR LSTM-BASED RIVER WATER LEVEL AND DISCHARGE FORECASTING A. Alzhanov & A. Nugumanova
190. A hybrid statistical-dynamical forecast of seasonal streamflow for a catchment in the Upper Columbia River basin in Canada T. Swift-LaPointe et al.
191. Quantum-enhanced deep learning for severe weather prediction: a 10-qubit QCNN-LSTM for bow echo forecasting H. Canot et al.
192. Machine Learning in Assessing the Performance of Hydrological Models E. Rozos et al.
193. PhysWRNet: A physics-guided deep learning framework for flood inundation mapping with SAR and hydrodynamic simulations L. Li et al.
194. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al.
195. Physically Consistent Runoff Simulation in Mountainous Catchments Using a Time-Varying Gated Hybrid XAJ–LSTM Model H. Shen et al.
196. A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion A. Sun et al.
197. RODEO: An algorithm and Google Earth Engine application for river discharge retrieval from Landsat R. Riggs et al.
198. Water Level Prediction and Forecasting Using a Long Short-Term Memory Model for Nam Ngum River Basin in Lao PDR C. Kim et al.
199. Construction of a daily streamflow dataset for Peru using a similarity-based regionalization approach and a hybrid hydrological modeling framework H. Llauca et al.
200. The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management V. Kumar et al.
201. Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden C. Brenner et al.
202. From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale? W. Zhi et al.
203. A differentiability-based processes and parameters learning hydrologic model for advancing runoff prediction and process understanding C. Zhang et al.
204. CNSW 1.0: Prefectural Reconstruction of China’s Surface Water Resources Using Machine Learning Methods Q. Wang et al.
205. Enhancing streamflow predictions through basin-to-basin knowledge transfer: A novel strategy for deep learning models adaptation and generalization K. Nifa et al.
206. Machine learning model for stage-discharge curve calculation J. Langhammer et al.
207. Evaluating Rainfall-Runoff Generation Mechanisms of Deep Learning Models Using a Process-Based Rainfall-Runoff Model T. Duong et al.
208. Soil moisture as a key predictor for regional groundwater levels: a deep learning study from Brandenburg, Germany M. Eckert & A. Rudolph
209. Adaptability of machine learning methods and hydrological models to discharge simulations in data-sparse glaciated watersheds H. Ji et al.
210. Global streamflow modelling using process-informed machine learning M. Magni et al.
211. Parsimonious statistical learning models for low-flow estimation J. Laimighofer et al.
212. Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis J. Martel et al.
213. Ergodic seismic precursors and transfer learning for short term eruption forecasting at data scarce volcanoes A. Ardid et al.
214. Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez
215. Flood Forecasting and Warning System: A Survey of Models and Their Applications in West Africa M. Fofana et al.
216. Blockchain consensus algorithm for supply chain information security sharing based on convolutional neural networks L. Cai et al.
217. On the interpretability of machine and deep learning techniques for predicting CBR of stabilized soil containing agro-industrial wastes S. Ghorbanzadeh et al.
218. Variational Bayesian dropout with a Gaussian prior for recurrent neural networks application in rainfall–runoff modeling S. Sadeghi Tabas & S. Samadi
219. Cyberinfrastructure for sustainability sciences C. Song et al.
220. Higher-order internal modes of variability imprinted in year-to-year California streamflow changes S. Duan et al.
221. Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats J. Liu et al.
222. Machine learning shows a limit to rain-snow partitioning accuracy when using near-surface meteorology K. Jennings et al.
223. CNN-Based Subnetworks for Improved Modelling of Extreme Flood and Drought Events in the Negro River Basin C. Eleutério et al.
224. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al.
225. HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network T. Nguyen et al.
226. Evaluating the Hydrological Applicability of Satellite Precipitation Products Using a Differentiable, Physics-Based Hydrological Model in the Xiangjiang River Basin, China S. Yan et al.
227. 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.
228. Improving Daily Streamflow Predictions over Large Watersheds: Introducing a Novel Enhanced Long Short-Term Memory (En-LSTM) Model P. Sharma et al.
229. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model H. Yin et al.
230. Multi-Source Data Integration and Model Coupling for Watershed Eco-Assessment Systems: Progress, Challenges, and Prospects L. Ma et al.
231. Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics Y. Wang et al.
232. An ensemble CNN-LSTM and GRU adaptive weighting model based improved sparrow search algorithm for predicting runoff using historical meteorological and runoff data as input Z. Yao et al.
233. RETRACTED ARTICLE: Green roofs and their effect on architectural design and urban ecology using deep learning approaches C. Wang et al.
234. DeepDiscover: towards autonomous discovery of bucket-type conceptual models – a proof of concept applied to hydrology A. Adombi
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242. Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany S. Kunz et al.
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247. Introductory overview: Recommendations for approaching scientific visualization with large environmental datasets C. Kelleher & A. Braswell
248. Potential of the Coupled WRF/WRF-Hydro Modeling System for Flood Forecasting in the Ouémé River (West Africa) G. Quenum et al.
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251. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al.
252. Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network D. Li et al.
253. Assessing Hydropower Impacts on Flood and Drought Hazards in the Lancang–Mekong River Using CNN-LSTM Machine Learning M. Zhang et al.
254. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River J. Xiong et al.
255. Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions J. Zwart et al.
256. Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions C. He et al.
257. From calibrated risk to actionable response: A prescriptive analytics pipeline for urban flash floods H. Gupta et al.
258. Hybrid modeling for daily streamflow forecasting: A study over the contiguous United States F. Zeng et al.
259. When good signatures go bad: Applying hydrologic signatures in large sample studies H. McMillan et al.
260. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al.
261. Meta-LSTM in hydrology: Advancing runoff predictions through model-agnostic meta-learning K. Cai et al.
262. Predicting annual peak daily streamflow in natural basins using quantile regression forests K. Kim et al.
263. Quantifying multi-year hydrological memory with Catchment Forgetting Curves A. de Lavenne et al.
264. Testing a low-complexity spatially distributed model to simulate the intra-annual dynamics of soil erosion and sediment delivery F. Matthews et al.
265. The potential of data driven approaches for quantifying hydrological extremes S. Hauswirth et al.
266. Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics J. Frame et al.
267. 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.
268. Better continental-scale streamflow predictions for Australia: LSTM as a land surface model post-processor and standalone hydrological model A. Shokri et al.
269. Hydro–Meteorological Coupled Runoff Forecasting Using Multi-Model Precipitation Forecasts Z. Zhu et al.
270. Groundwater Storage Variations in the Huadian Photovoltaic Base of the Tengger Desert Based on Machine Learning–Downscaled GRACE Data R. Chen et al.
271. Comparison of Deep Learning Techniques for River Streamflow Forecasting X. Le et al.
272. Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić
273. Rainfall-runoff modeling using long short-term memory based step-sequence framework H. Yin et al.
274. Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions H. Tao et al.
275. Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data S. Gharari et al.
276. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan
277. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al.
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279. A Comprehensive Calibration Framework for the Northwest River Forecast Center G. Walters et al.
280. A novel approach for river discharge prediction in Lancang Mekong River Basin: Incorporation of multisource remote sensing and LSTM model T. Aryal et al.
281. Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020) J. Willard et al.
282. Machine-learning methods for stream water temperature prediction M. Feigl et al.
283. Remote Sensing of Water: The Observation-to-Inference Arc Across Six Decades and Toward an AI-Native Future D. Ames
284. Sensor Networks, Data Processing, and Inference: The Hydrology Challenge A. Zanella et al.
285. A Review of XAI Methods Applications in Forecasting Runoff and Water Level Hydrological Tasks A. Bramm et al.
286. A multi-model evaluation of probabilistic streamflow predictions via residual error modelling J. Romero-Cuellar et al.
287. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al.
288. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al.
289. Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources J. Willard et al.
290. Modelling Temporal Dynamics and Uncertainty in River Water Levels Using TP‐Transformer: Advancing Hydrological Process Understanding F. Yao et al.
291. Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States H. Cai et al.
292. On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider
293. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al.
294. Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks S. Clark et al.
295. Comparison and integration of hydrological models and machine learning models in global monthly streamflow simulation J. Zhang et al.
296. Mitigating Prediction Error of Deep Learning Streamflow Models in Large Data‐Sparse Regions With Ensemble Modeling and Soft Data D. Feng et al.
297. Global patterns of gully occurrence and their sensitivity to environmental changes Y. Chen et al.
298. Soil Moisture to Runoff (SM2R): A Data‐Driven Model for Runoff Estimation Across Poorly Gauged Asian Water Towers Based on Soil Moisture Dynamics X. Li et al.
299. Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning Ž. Brkić & M. Kuhta
300. Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism M. Kordani et al.
301. Towards Predicting Flood Event Peak Discharge in Ungauged Basins by Learning Universal Hydrological Behaviors with Machine Learning A. Sanjay Potdar et al.
302. From Climate Signals to Smart Predictions: Advanced Machine Learning and Deep Learning for Hydrological Modeling in Data-Scarce Bangladesh M. Islam et al.
303. Analyzing the Impact of High-Frequency Noise on Hydrological Runoff Modeling: A Frequency-Based Framework for Data Uncertainty Assessment T. Liu et al.
304. Quantifying Streamflow Depletion from Groundwater Pumping: A Practical Review of Past and Emerging Approaches for Water Management S. Zipper et al.
305. A python framework for differentiable hydrological modeling and research workflow automation W. Ouyang et al.
306. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al.
307. Study on Water Quality Prediction of Urban Reservoir by Coupled CEEMDAN Decomposition and LSTM Neural Network Model L. Zhang et al.
308. When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al.
309. Identifying Climate Change Impacts On Hydrological Behavior On Large-Scale With Machine Learning Algorithms A. Ivanov et al.
310. Advancing the science of headwater streamflow for global water protection H. Golden et al.
311. Influence of Meteorological Parameters on Explosive Charge and Stemming Length Predictions in Clay Soil during Blasting Using Artificial Neural Networks K. Leskovar et al.
312. Quantifying regional variability of machine-learning-based snow water equivalent estimates across the Western United States D. Liljestrand et al.
313. Regional Drivers of Stream Chemical Behavior: Leveraging Lithology, Land Use, and Climate Gradients Across the Colorado River, Texas USA G. Goldrich‐Middaugh et al.
314. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al.
315. Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al.
316. Improving Hydrological Modeling with Hybrid Models: A Comparative Study of Different Mechanisms for Coupling Deep Learning Models with Process-based Models Y. Wei et al.
317. Evaluating rainfall-driven LSTM for statistical analysis of low-flow regimes in the Nakagawa basin, Japan D. Kim & N. Shirakawa
318. Artificial intelligence with earth observations provides continuous streamflow data across varying wildfire recurrence and recovery scenarios S. Uddin et al.
319. Sensors prioritisation for hydrological forecasting based on interpretable machine learning A. Menapace et al.
320. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al.
321. Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate Z. Rasheed et al.
322. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al.
323. Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes V. Tran et al.
324. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang
325. Interpretable feature incorporation machine-learning framework for flood magnitude estimation E. Ford et al.
326. A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al.
327. A review of hydrologic signatures and their applications H. McMillan
328. Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions S. Huang et al.
329. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al.
330. Innovative daily runoff prediction model integrating black-winged kite algorithm and Mamba2–Transformer architecture D. Xu et al.
331. Evaluation of Transformer model and Self-Attention mechanism in the Yangtze River basin runoff prediction X. Wei et al.
332. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning F. Kratzert et al.
333. Enhancing generalizability of data-driven urban flood models by incorporating contextual information T. Cache et al.
334. Enhancing runoff prediction accuracy of deep learning model using baseflow separation method and timestamp information C. Jin et al.
335. A Coupled Framework of Reservoir Operation and SAC-SMA Hydrological Model for River Basin Simulation T. Ngo et al.
336. Inductive predictions of hydrologic events using a Long Short-Term Memory network and the Soil and Water Assessment Tool N. Majeske et al.
337. Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems V. Srikrishnan et al.
338. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al.
339. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al.
340. Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo
341. Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China Y. Man et al.
342. A review of machine learning methods for geopolymer performance prediction: Linking data-driven models with physical insights S. Zheng et al.
343. Data-driven effects of human activities and environmental factors on inland aquatic dissolved organic matter in China: Insights from machine learning Y. Ma et al.
344. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al.
345. Integrating Regressive and Probabilistic Streamflow Forecasting via a Hybrid Hydrological Forecasting System: Application to the Paraíba do Sul River Basin G. França et al.
346. Addressing hydrological modeling in watersheds under land cover change with deep learning D. Althoff et al.
347. Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir
348. Towards a comprehensive uncertainty assessment in environmental research and decision support P. Reichert
349. Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model R. Chlumsky et al.
350. Global evapotranspiration simulation research using a coupled deep learning algorithm with physical mechanisms Y. Sun et al.
351. Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić
352. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al.
353. High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al.
354. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al.
355. Modeling continental US stream water quality using long-short term memory and weighted regressions on time, discharge, and season K. Fang et al.
356. Machine learning for hydrologic sciences: An introductory overview T. Xu & F. Liang
357. From short to long-term horizons: Comparative assessment of Machine Learning and Deep Learning for river stage prediction with spatial transferability analysis M. Islam et al.
358. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al.
359. Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers W. Zhi et al.
360. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan
361. Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: A case study of three mountainous areas on the Tibetan Plateau B. Li et al.
362. Deep-Learning-Based Probabilistic Forecasting of Groundwater Storage Dynamics in Sudan Using Multisource Remote Sensing and Geophysical Data M. Mohammed et al.
363. From Rivers to Floodplains: Leveraging Transfer Learning to Predict Floodplain Dissolved Oxygen G. Myers et al.
364. Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model J. Wu et al.
365. Blending-ensemble LSTM-SVR Modeling for Streamflow Prediction with Limited Data Series Based on Tree-structured Parzen Estimator Bayesian Optimization J. Zhang et al.
366. Global soil moisture data derived through machine learning trained with in-situ measurements S. O. & R. Orth
367. Using a physics-based hydrological model and storm transposition to investigate machine-learning algorithms for streamflow prediction F. Gurbuz et al.
368. Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al.
369. Reliable and Adaptive Probabilistic Forecasting for Event-Driven Water-Quality Time Series Using a Gated Hybrid–Mixture Density Network N. Ehmimed et al.
370. Hourly Regional Rainfall–Runoff Prediction Using Transformer with Water Conservation Constraints G. Jing et al.
371. A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling F. Kratzert et al.
372. GeoFormer: Geography-Aware Adaptive Transformer with Multi-Scale Temporal Fusion for Global Reservoir Water Level Forecasting X. Wu et al.
373. Leveraging synthetic assimilation of remote sensing with the National Water Model (NWM) to improve above-normal flow prediction in ungauged basins S. Fang et al.
374. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al.
375. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al.
376. Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al.
377. High-frequency data significantly enhances the prediction ability of point and interval estimation X. Liu et al.
378. An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system W. Zhu et al.
379. Forecasting change in groundwater storage: A multilayer perceptron model leveraging system dynamics simulation data G. Eslamifar et al.
380. An intelligent VMD-WTC-GRU hybrid framework with uncertainty quantification for forecasting extreme flood events in semi-arid regions R. Ramezani et al.
381. A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data J. Liu et al.
382. A rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction Y. Zhou et al.
383. Developing a stochastic hydrological model for informing lake water level drawdown management X. He et al.
384. A parsimonious setup for streamflow forecasting using CNN-LSTM S. Pokharel & T. Roy
385. Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): streamflow observations, forcing data and geospatial data for hydrologic studies across North America W. Knoben et al.
386. Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX) A. Wunsch et al.
387. Utilization of the Long Short-Term Memory network for predicting streamflow in ungauged basins in Korea J. Choi et al.
388. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al.
389. An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis Y. Ma et al.
390. A coupled model applied to complex river–lake systems Q. Zhang et al.
391. Physics-encoded deep learning for integrated modeling of watershed hydrology and reservoir operations B. Yu et al.
392. Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell E. Acuña Espinoza et al.
393. A hybrid data-driven approach to analyze the drivers of lake level dynamics M. Somogyvári et al.
394. Improving high uncertainty of evapotranspiration products under extreme climatic conditions based on deep learning and ERA5 reanalysis data L. Qian et al.
395. A novel operational water quality mobile prediction system with LSTM-Seq2Seq model L. Xie et al.
396. Deep learning can facilitate physically interpretable geoscientific modelling H. Gupta
397. 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.
398. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy
399. Water Quality Prediction Based on Physical and Ecological Constraints Using Multi-Model Fusion: A Robust End-to-End Mechanism from Rule-Based Adjudication to Online Backoff L. Ma et al.
400. A research agenda for improving flood resilience in Australia F. Johnson & S. Hettiarachchi
401. CAMELS-NZ: hydrometeorological time series and landscape attributes for New Zealand S. Bushra et al.
402. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al.
403. Deep learning model on rates of change for multi-step ahead streamflow forecasting W. Tan et al.
404. Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural Networks W. Jiang et al.
405. Improving prediction of class-imbalanced time series through curation of training data: A case study of frozen ground prediction M. Ghosh et al.
406. Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems A. Rupe et al.
407. Issues in generating stochastic observables for hydrological models K. Beven
408. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al.
409. Uncertainty in Environmental Micropollutant Modeling H. Ahkola et al.
410. Water Resources’ AI–ML Data Uncertainty Risk and Mitigation Using Data Assimilation N. Martin & J. White
411. Neural networks in catchment hydrology: a comparative study of different algorithms in an ensemble of ungauged basins in Germany M. Weißenborn et al.
412. An LSTM network for joint modeling of streamflow and hydropower generation for run-of-river plants T. Roksvåg et al.
413. Augmenting geophysical interpretation of data-driven operational water supply forecast modeling for a western US river using a hybrid machine learning approach S. Fleming et al.
414. Short-term optimal scheduling rules extraction for hydro-solar-pumped storage complementary power systems based on orthogonal experimental design and a physics-constrained LSTM model L. Zhu et al.
415. Long-term Reservoir Inflow Forecasts: Enhanced Water Supply and Inflow Volume Accuracy Using Deep Learning Z. Herbert et al.
416. Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models A. Jamaat et al.
417. Surface and sub-surface flow estimation at high temporal resolution using deep neural networks A. Abbas et al.
418. Runoff predictions in ungauged basins using sequence-to-sequence models H. Yin et al.
419. Optimization-Driven Learning for Leakage-Controlled Geospatial Modeling of Antenna Structure Registration Data S. Alhammad et al.
420. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al.
421. Improving trans-regional hydrological modelling by combining LSTM with big hydrological data S. Tang et al.
422. Improving differentiable hydrologic modeling with interpretable forcing fusion K. Sawadekar et al.
423. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al.
424. A comparison between numerical, neural network, and hybrid modelling approaches to simulate spring flow from a karst catchment in northwest Ireland using long-term hydrological data N. Chowdhury et al.
425. A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al.
426. Estimating Lake Water Volume With Regression and Machine Learning Methods C. Delaney et al.
427. Reconstructing daily streamflow and floods from large-scale atmospheric variables with feed-forward and recurrent neural networks in high latitude climates J. Hagen et al.
428. Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence T. Schneider et al.
429. Enhancing representation of data-scarce reservoir-regulated river basins using a hybrid DL-process based approach L. Deng et al.
430. A comparative study between time series and soft computing models for river discharge forecasting H. Hamzeh et al.
431. Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy D. Feng et al.
432. Enhancing monthly runoff prediction in arid alpine basins of northwestern China by an EMD-PCA-LSTM hybrid model W. Hu et al.
433. Forecasting Floods Using Deep Learning Models: A Longitudinal Case Study of Chenab River, Pakistan K. Aatif et al.
434. Large contribution of antecedent climate to ecosystem productivity anomalies during extreme events J. Qiu et al.
435. Machine Learning Improvement of Streamflow Simulation by Utilizing Remote Sensing Data and Potential Application in Guiding Reservoir Operation S. He et al.
436. From big data to small scales: Machine learning enhances microclimate model predictions A. Itzkovitch et al.
437. Regarding reservoirs as switches: Deriving restored and regulated runoff with one long short-term memory model H. Ye et al.
438. Improving the prediction of daily reservoir releases over the CONUS using conditioned LSTM H. Tran et al.
439. A dataset of land surface characteristics and time-series hydrometeorological data for typical catchments in China (2003–2020) H. MA et al.
440. Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction P. Huang
441. Transfer learning using the global Caravan dataset for developing a local river streamflow prediction model A. Alzhanov et al.
442. A brief history of information and disinformation in hydrological data and the impact on the evaluation of hydrological models K. Beven
443. Uneven shifts in flood and drought flows in a Brazilian water supply catchment T. Mattos et al.
444. Causality-Guided Deep learning for streamflow predicting in a mountainous region X. Tang et al.
445. River flooding mechanisms and their changes in Europe revealed by explainable machine learning S. Jiang et al.
446. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn
447. Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty
448. Artificial intelligence can provide accurate forecasts of extreme floods at global scale
449. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen
450. Strategies for incorporating static features into global deep learning models T. Liesch & M. Ohmer
451. Data-Driven Dam Outflow Prediction Using Deep Learning with Simultaneous Selection of Input Predictors and Hyperparameters Using the Bayesian Optimization Algorithm V. Tran et al.
452. Development of a physics-informed data-driven model for gaining insights into hydrological processes in irrigated watersheds K. Li et al.
453. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al.
454. Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al.
455. Hybrid deep learning-numerical modeling framework for long-term prediction of groundwater discharge and radionuclide transport M. Seong et al.
456. Deep Learning for Isotope Hydrology: The Application of Long Short-Term Memory to Estimate High Temporal Resolution of the Stable Isotope Concentrations in Stream and Groundwater A. Sahraei et al.
457. An interpretable and scalable model for rapid flood extent forecasting using satellite imagery and machine learning with rotated EOF analysis H. Wan et al.
458. Toward routing river water in land surface models with recurrent neural networks M. Lima et al.
459. Streamflow forecasting with a hybrid model integrating a distributed hydrological model and deep learning methods X. Liang et al.
460. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya T. Lees et al.
461. Daily Runoff Forecasting in the Middle Yangtze River Using a Long Short-Term Memory Network Optimized by the Sparrow Search Algorithm Q. Zhang et al.
462. Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al.
463. A highly generalizable data-driven model for spatiotemporal urban flood dynamics real-time forecasting based on coupled CNN and ConvLSTM W. Lou et al.
464. Comparative assessment of hydrological and deep learning models for runoff simulation and water storage in irrigated basins A. Razeghi Haghighi et al.
465. A novel hybrid fine-tuning method for supercharging deep learning model development for hydrological prediction M. Jahangir et al.
466. Global prediction of extreme floods in ungauged watersheds G. Nearing et al.
467. Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment D. Althoff & L. Rodrigues
468. Improving PM2.5 simulations using LSTM: a study on spatiotemporal generalization X. Chen et al.
469. A heterogeneous weighting strategy for leveraging Cross-Basin data enhances the Usability of deep learning hydrological models S. Yoon et al.
470. On the future of hydroecological models of everywhere K. Beven
471. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al.
472. A Mamba-type of deep state space model for reservoir release simulation with a large-scale verification over 441 dams across CONUS J. Zhang et al.
473. Deep learning for water quality W. Zhi et al.
474. Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures A. Gupta et al.
475. A novel remote sensing-based calibration and validation method for distributed hydrological modelling in ungauged basins D. Zheng et al.
476. Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E) J. Mai et al.
477. Exploring the role of the long short‐term memory model in improving multi‐step ahead reservoir inflow forecasting X. Luo et al.
478. Flood forecasting with machine learning models in an operational framework S. Nevo et al.
479. Synergistic approach for streamflow forecasting in a glacierized catchment of western Himalaya using earth observation and machine learning techniques J. Dharpure et al.
480. Climate change impact assessment on a German lowland river using long short-term memory and conceptual hydrological models A. Ley et al.
481. Multi-step ahead forecasting of daily streamflow based on the transform-based deep learning model under different scenarios M. He et al.
482. Regional scale simulations of daily suspended sediment concentration at gauged and ungauged rivers using deep learning A. Gupta & D. Feng
483. Physics-aware machine learning revolutionizes scientific paradigm for process-based modeling in hydrology Q. Xu et al.
484. Synergizing machine learning and modified physical models for hydrology modeling: A case study of modified SIMHYD and TANK models X. Lei et al.
485. Predicting runoff series in ungauged basins of the Brazilian Cerrado biome D. Althoff et al.
486. Time to Update the Split‐Sample Approach in Hydrological Model Calibration H. Shen et al.
487. Deep learning rainfall–runoff predictions of extreme events J. Frame et al.
488. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al.
489. CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany R. Loritz et al.
490. Runoff simulation driven by multi-source satellite data based on hydrological mechanism algorithm and deep learning network C. Yu et al.
491. Optimization of LSTM Parameters for Flash Flood Forecasting Using Genetic Algorithm Y. Jhong et al.
492. Perceptual perplexity and parameter parsimony K. Beven & N. Chappell
493. Development and Application of a Distributed Hydrological Model Ensemble (DHM-FEWS) for Flash Flood Early Warning X. Liu et al.
494. Quantitative study of rainfall lag effects and integration of machine learning methods for groundwater level prediction modelling Y. Wang et al.
495. A global dataset of reservoir in-situ water levels for hydrological and remote sensing applications M. Zhang et al.
496. Deep Learning‐Based Rapid Flood Inundation Modeling for Flat Floodplains With Complex Flow Paths Y. Zhou et al.
497. From climate signals to smart predictions: advanced machine learning and deep learning for hydrological modeling in data-scarce Bangladesh M. Islam et al.
498. DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States P. Ghaneei & H. Moradkhani
499. Comparison and interpretation of data-driven models for simulating site-specific human-impacted groundwater dynamics in the North China Plain H. Jing et al.
500. AI-driven improvement of monthly average rainfall forecasting in Mecca using grid search optimization for LSTM networks F. Alqahtani
501. Dive into transfer-learning for daily rainfall-runoff modeling in data-limited basins W. Ouyang et al.
502. Monthly streamflow forecasting with temporal-periodic transformer H. Yin et al.
503. Urban Flood Prediction Model Based on Transformer-LSTM-Sparrow Search Algorithm Z. Fan et al.
504. CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland B. Kraft et al.
505. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al.
506. Comparative analysis of deep learning and machine learning models for one-day-ahead streamflow forecasting in the Krishna River basin S. Kaur & S. Chavan
507. What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al.
508. Hybrid forecasting: blending climate predictions with AI models L. Slater et al.
509. Influence of cascade reservoir operation in the Upper Mekong River on the general hydrological regime: A combined data-driven modeling approach X. Yuan et al.
510. Assessing the adequacy of traditional hydrological models for climate change impact studies: a case for long short-term memory (LSTM) neural networks J. Martel et al.
511. A DeepAR-Based Modeling Framework for Probabilistic Mid–Long-Term Streamflow Prediction S. Xie et al.
512. Multi-step regional rainfall-runoff modeling using pyramidal transformer H. Yin et al.
513. Development of a Long-Range Hydrological Drought Prediction Framework Using Deep Learning M. Khan & R. Maity
514. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al.
515. Long short-term memory integrating moving average method for flood inundation depth forecasting based on observed data in urban area S. Yang et al.
516. Exploring a process-aware spatiotemporal graph-based surrogate for integrated urban drainage simulation B. Yin et al.
517. A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al.
518. Persistent neural calibration for discharges modelling in drought-stressed catchments I. Pulido-Calvo et al.
519. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships K. Xie et al.
520. Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis J. Dharpure et al.
521. Enhancing runoff prediction with causal lag-aware attention and multi-scale fusion in transformer models W. Yuan & H. Yan
522. UK Hydrological Outlook using Historic Weather Analogues W. Chan et al.
523. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al.
524. Exploring soil moisture dynamics and variability across scales and geological settings using gaussian mixture-long short-term memory networks B. Bischof et al.
525. Assessing the Risk of Natural and Socioeconomic Hazards Caused by Rainfall in the Middle Yellow River Basin Y. Zhao et al.
526. Improving evapotranspiration estimation by integrating process-based biophysical variables into a deep learning approach M. Xie et al.
527. A coupled spatial reduction-reconstruction and LSTM framework (SRR-LSTM) for groundwater level prediction in large irrigation districts H. Wei et al.
528. Enhancing flood prediction through physics-driven typhoon feature engineering and machine learning Z. Zhang et al.
529. Hybrid deep learning approach for multi-step-ahead prediction for daily maximum temperature and heatwaves M. Khan & R. Maity
530. Assessment of drought conditions and prediction by machine learning algorithms using Standardized Precipitation Index and Standardized Water-Level Index (case study: Yazd province, Iran) R. Shakeri et al.
531. Daily runoff prediction for ungauged basins: Optimization of model transfer and selection of donor basins X. Song et al.
532. Assessing the transferability of LSTM-based streamflow models under varying source basin diversity and target data availability (Mangla Basin, Pakistan) M. Adnan et al.
533. Explainable deep learning-based simulation for evaluating climate-driven future groundwater level changes in South Korea J. Hwang & K. Lee
534. How much historical data do we need? The role of data recency and training period length in LSTM-based rainfall-runoff modeling Q. Yu & B. Tolson
535. Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions D. Klotz et al.
536. Metamodeling of a physically based pesticide runoff model with a long short-term memory approach G. Métayer et al.
537. Value of process understanding in the era of machine learning: A case for recession flow prediction P. Istalkar et al.
538. Flood Uncertainty Estimation Using Deep Ensembles P. Chaudhary et al.
539. Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S. Y. Yang et al.
540. Advancing stream temperature prediction with a generalizable large-sample framework across CONUS river reaches M. Bakar Siddik et al.
541. Event-based training data thresholds for BiLSTM versus Xinanjiang models: Insights from the applications of 19 Chinese catchments Y. Li et al.
542. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al.
543. Evaluating different machine learning methods to simulate runoff from extensive green roofs E. Abdalla et al.
544. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al.
545. Deep learning based multi-model ensemble approach for flood forecasting over flood prone rivers of India M. Modi et al.
546. Long-Lead Forecasting of Runoff Season Flows in the Colorado River Basin Using a Random Forest Approach D. Woodson et al.
547. Machine learning in stream and river water temperature modeling: a review and metrics for evaluation C. Corona & T. Hogue
548. How do geological map details influence the identification of geology-streamflow relationships in large-sample hydrology studies? T. do Nascimento et al.
549. Deep learning, hydrological processes and the uniqueness of place K. Beven
550. Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism L. Girihagama et al.
551. Machine learning insights for fire impacts on snow disappearance predictability in Northern California Y. Mohanty et al.
552. Training machine learning with physics-based simulations to predict 2D soil moisture fields in a changing climate E. Leonarduzzi et al.
553. A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al.
554. Probabilistic Sub‐Daily Streamflow Forecasting in Data‐Scarce Catchments Through a Zero‐Shot Inductive Transfer Learning Approach E. Heidari & A. Khan
555. Effects of cascading reservoirs on streamflow and sediment load with machine learning reconstructed time series in the upper Yellow River basin J. Fan et al.
556. Dynamic Assimilation of Deep Learning Predictions to a Process-Based Water Budget N. Martin
557. 无资料流域日径流预测<bold>:</bold> 模型迁移优化及参考流域决策分析 昕. 宋 et al.
558. On the challenges of global entity-aware deep learning models for groundwater level prediction B. Heudorfer et al.
559. Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks G. Ayzel et al.
560. Understanding the Way Machines Simulate Hydrological Processes—A Case Study of Predicting Fine-Scale Watershed Response on a Distributed Framework D. Kim et al.
561. Revealing the causes of groundwater level dynamics in seasonally frozen soil zones using interpretable deep learning models H. Li et al.
562. Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al.
563. Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions A. Gupta & S. McKenna
564. Toward interpretable LSTM-based modeling of hydrological systems L. De la Fuente et al.
565. Mind the information gap: How sampling and clustering impact the predictability of reach‐scale channel types in California (USA) H. Guillon et al.
566. Numerical and LSTM-based modeling of physical clogging effects in geothermal injection wells M. Ben Aoun et al.
567. Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning M. He et al.