196 citations as recorded by crossref.

1. Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting K. Lin et al.
2. River flooding mechanisms and their changes in Europe revealed by explainable machine learning S. Jiang et al.
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6. The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia M. Mohammadi et al.
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12. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al.
13. How extreme hydrological events correspond to climate extremes in the context of global warming: A case study in the Luanhe River Basin of North China G. Gao et al.
14. Collaborative Station Learning for Rainfall Forecasting B. Patro & P. Bartakke
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17. High flow prediction model integrating physically and deep learning based approaches with quasi real-time watershed data assimilation M. Jeong et al.
18. Machine learning advances and data model coevolution in geoscience A. Eltijnai & M. Mohammed
19. Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest J. Dias et al.
20. Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al.
21. 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.
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25. Multisite evaluation of physics-informed deep learning for permafrost prediction in the Qinghai-Tibet Plateau Y. Liu et al.
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27. Advancement of a Blended Hydrologic Model for Robust Model Performance R. Chlumsky et al.
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29. Global prediction of extreme floods in ungauged watersheds G. Nearing et al.
30. Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments Y. Zhang et al.
31. Assessing the transferability of LSTM-based streamflow models under varying source basin diversity and target data availability (Mangla Basin, Pakistan) M. Adnan et al.
32. Strategies for incorporating static features into global deep learning models T. Liesch & M. Ohmer
33. Accuracy of HEC-HMS and Artificial Neural Network models in simulating runoffs in upper valley of the Medjerda-Tunisia M. Kotti & T. Hermassi
34. Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data K. Tayal et al.
35. An improved dynamics framework for accurate multi-step ahead daily streamflow prediction with spatial–temporal and global information L. Qian et al.
36. Flood forecasting with machine learning models in an operational framework S. Nevo et al.
37. A Nudge to the Truth: Atom Conservation as a Hard Constraint in Models of Atmospheric Composition Using a Species-Weighted Correction P. Sturm & S. Silva
38. A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model S. Jia et al.
39. Uneven shifts in flood and drought flows in a Brazilian water supply catchment T. Mattos et al.
40. A Hybrid Muskingum–Machine Learning Flood Forecasting Model: Application and Evaluation in the Tarim River Basin P. Wang et al.
41. A hybrid statistical-dynamical forecast of seasonal streamflow for a catchment in the Upper Columbia River basin in Canada T. Swift-LaPointe et al.
42. Bayesian optimization for uncertainty-aware prediction of rainfall-induced deformation in embankment dams M. Nasser et al.
43. Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost G. Gorski et al.
44. Time fractional Saint Venant equations reveal the physical basis of hydrograph retardation through model comparison and field data H. Wei et al.
45. A Community Dataset for Large-Scale River Nitrogen Modeling in the United States S. Chang et al.
46. HieraBoost-Q: interpretable karst discharge prediction from multi-site electrical conductivity with SHAP-based mechanism insights Y. Zhu et al.
47. Solving flood problems with deep learning technology: Research status, strategies, and future directions H. Li et al.
48. Development and implementation of a passive surveillance system for Aedes albopictus in Emilia-Romagna, Italy M. Blaha et al.
49. From Rivers to Floodplains: Leveraging Transfer Learning to Predict Floodplain Dissolved Oxygen G. Myers et al.
50. Machine Learning-Based Flood Forecasting System for Window Cliffs State Natural Area, Tennessee G. Darkwah et al.
51. Multi-site deep learning for groundwater level prediction across global datasets: toward scalable applications under data scarcity A. Nolte et al.
52. Towards more accurate very short-range flood forecasts with physics-enhanced machine learning models R. Bezerra et al.
53. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al.
54. The Potential of EO Data for Enhanced Flood Monitoring and Forecasting: A Consortium Assessment A. Tarpanelli et al.
55. Hydrological insights from a comparative evaluation of LSTM and MC-LSTM networks in the Ebro River basin (Spain) I. González-Planet & C. Juez
56. A review of hybrid deep learning applications for streamflow forecasting K. Ng et al.
57. A hybrid CNN-LSTM surrogate model for hyper-resolution spatiotemporal flood forecasting in Norfolk, Virginia Y. Wang et al.
58. SMamba-KAN: an advanced temporal-nonlinear model for precise water level prediction X. Yan et al.
59. From risk control to resilience: developments and trends of urban roads designed as surface flood passages to cope with extreme storms Z. Shao et al.
60. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al.
61. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al.
62. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al.
63. Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz
64. Enhancing flood prediction in the Lower Mekong River Basin by a scale-independent interpretable deep learning model Y. Qiu et al.
65. Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al.
66. Gradient-informed data splitting and model setup for machine learning prediction of storm surge Y. Wang et al.
67. RFM_Trans: Runoff forecasting model for catchment flood protection using strategies optimized Transformer N. Bao et al.
68. A spatiotemporally differentiated hybrid hydrological modeling strategy with dynamically adaptive runoff generation modes X. Ma et al.
69. A new stable and interpretable flood forecasting model combining multi-head attention mechanism and multiple linear regression Y. Wang et al.
70. The effectiveness of machine learning methods in the nonlinear coupled data assimilation Z. Xuan et al.
71. Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM Y. Hu et al.
72. Enhanced Time Series–Physics Model Approach for Dam Discharge Impacts on River Levels: Seomjin River, South Korea C. Jung et al.
73. ER-MACG: An Extreme Precipitation Forecasting Model Integrating Self-Attention Based on FY4A Satellite Data M. Lu et al.
74. Improving multi-seasonal prediction, extreme weather events and climate analyses over the southwest coastal region (SCR) of Indian Ocean Dipole using advanced machine learning techniques S. Kumari & P. Muthulakshmi
75. APPLICATION OF DEEP LEARNING TO DAM INFLOW FORECASTS WITH DIFFERENT CHARACTERISTICS IN THE CHUBU AREA AND THE EFFECT OF FORECAST ACCURACY CAUSED BY MIX OF INPUT RAINFALL TYPES T. KUREBAYASHI et al.
76. Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H. Gholizadeh et al.
77. An LSTM network for joint modeling of streamflow and hydropower generation for run-of-river plants T. Roksvåg et al.
78. Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty S. Rahat et al.
79. Streamflow forecast model using ANN D. Sonowal et al.
80. Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam T. Thach
81. The Effect of Climatic Variability on Consumer Prices: Evidence from El Niño–Southern Oscillation Indices J. Park & S. Byeon
82. Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments P. Bhasme & U. Bhatia
83. A compound approach for ten-day runoff prediction by coupling wavelet denoising, attention mechanism, and LSTM based on GPU parallel acceleration technology Y. Wang et al.
84. MonoGRU: A theoretical and empirical evaluation of a streamlined gated recurrent unit for rainfall forecasting M. Shakib et al.
85. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al.
86. Machine learning-enabled calibration of river routing model parameters Y. Zhao et al.
87. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés
88. Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia) I. Leščešen et al.
89. Bayesian model averaging by combining deep learning models to improve lake water level prediction G. Li et al.
90. Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches S. Latif et al.
91. Improving Monthly Streamflow Prediction by Deep Learning Model With Physics‐Based Rules L. Ni et al.
92. Operational low-flow forecasting using LSTMs J. Deng et al.
93. Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer R. Wu et al.
94. Unpacking the black box of deep learning for identifying El Niño-Southern oscillation Y. Sun et al.
95. Fluvial Dynamics and Hydrological Variability in the Chiriquí Viejo River Basin, Panama: An Assessment of Hydro-Social Sustainability through Advanced Hydrometric Indexes H. De Gracia et al.
96. Comparative assessment of machine learning models for daily streamflow prediction in a subtropical monsoon watershed Z. Zhang et al.
97. Validating the IMERG remote sensing precipitation data for extremes analysis using the new hybrid depth duration frequency model K. Ekpetere
98. An improved Hydrology-Informed attention LSTM(HIA-LSTM) model for runoff simulation with seasonal snowmelt M. Ling et al.
99. Evaluation of an inflow forecast correction method based on Multi-Scenarios division S. Wang et al.
100. Optimizing seasonal discharge predictions: a hybridized approach with AI and non-linear models S. Sharma & M. Patel
101. Data-driven modelling of hydraulic-head time series: results and lessons learned from the 2022 Groundwater Time Series Modelling Challenge R. Collenteur et al.
102. Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions? Y. Khoshkalam et al.
103. Ensembling differentiable process-based and data-driven models with diverse meteorological forcing datasets to advance streamflow simulation P. Li et al.
104. Bias learning improves data driven models for streamflow prediction Y. Lin et al.
105. Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources J. Willard et al.
106. How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models? R. Hashemi et al.
107. Application of LSTM considering time steps in runoff prediction of Ganjiang River Basin H. Leyi et al.
108. Two-step hybrid model for monthly runoff prediction utilizing integrated machine learning algorithms and dual signal decompositions S. Wu et al.
109. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al.
110. Soil Water Accounting Network (SWAN): a novel neural network for modeling conceptual hydrological processes Z. Fang et al.
111. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang
112. Improving Sub-daily Runoff Forecast Based on the Multi-objective Optimized Extreme Learning Machine for Reservoir Operation W. Jia et al.
113. Development of objective function-based ensemble model for streamflow forecasts Y. Lin et al.
114. Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning R. Palmitessa et al.
115. The representation of rivers in operational ocean forecasting systems: a review P. Matte et al.
116. Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modeling S. Houénafa et al.
117. Streamflow prediction using machine learning models in selected rivers of Southern India R. Sharma et al.
118. Hybrid process-based and deep learning for river nutrient prediction under limited monitoring data J. Tang et al.
119. 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
120. Does MC-LSTM model improve the reliability of streamflow prediction in human-influenced watersheds? G. Sahu et al.
121. Monthly Runoff Prediction Based on Stochastic Weighted Averaging-Improved Stacking Ensemble Model K. Fu et al.
122. 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.
123. Improving global soil moisture prediction based on Meta-Learning model leveraging Köppen-Geiger climate classification Q. Li et al.
124. Application of Deep Learning in Drainage Systems Monitoring Data Repair—A Case Study Using Con-GRU Model L. He et al.
125. LMCNN: A Lightweight Attention-Based Modern Convolutional Neural Network Design for Runoff Prediction H. Jiang et al.
126. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al.
127. Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al.
128. HydroGRAF: Hybrid discharge reconstruction and basin-aware streamflow forecasting in the Himalayas A. Gul et al.
129. Innovative daily runoff prediction model integrating black-winged kite algorithm and Mamba2–Transformer architecture D. Xu et al.
130. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al.
131. A hybrid NARX–LSTM neural network for predicting climate change impacts on snow cover area J. Hidalgo-Hidalgo et al.
132. Challenges of modelling climate change impacts on hydrology and water resources: AI is the game changer—a review C. Onyutha
133. Ensemble Streamflow Simulations in a Qinghai–Tibet Plateau Basin Using a Deep Learning Method with Remote Sensing Precipitation Data as Input J. Wang et al.
134. Multi-decadal streamflow projections for catchments in Brazil based on CMIP6 multi-model simulations and neural network embeddings for linear regression models M. Scheuerer et al.
135. Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir
136. Interpretable feature incorporation machine-learning framework for flood magnitude estimation E. Ford et al.
137. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al.
138. CNN-BiLSTM with attention mechanism and soil-aware dynamic EI-D thresholds for extreme rainfall-induced landslide early warning in subtropical mountains W. Xiao et al.
139. Hydro-informer: a deep learning model for accurate water level and flood predictions W. Almikaeel et al.
140. Spatial inequality of climate risks: Impacts of extreme rainfall and flooding under multiple climate scenarios in the Beijing-Tianjin-Hebei urban agglomeration, China Y. Zhang et al.
141. Nonlinear pollution source switching under extreme rainfall revealed by budget-closed graph-based apportionment G. Zhang et al.
142. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al.
143. Improving short-term streamflow forecasting by flow mode clustering S. Liu et al.
144. Using Entity-Aware LSTM to Enhance Streamflow Predictions in Transboundary and Large Lake Basins Y. Park et al.
145. Long short‐term memory model for predicting groundwater level in Alabama V. Robinson et al.
146. Predicting shoreline changes using deep learning techniques with Bayesian optimisation T. Manamperi et al.
147. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al.
148. Integrating Regressive and Probabilistic Streamflow Forecasting via a Hybrid Hydrological Forecasting System: Application to the Paraíba do Sul River Basin G. França et al.
149. Artificial intelligence with earth observations provides continuous streamflow data across varying wildfire recurrence and recovery scenarios S. Uddin et al.
150. A hybrid deep learning rainfall-runoff forecasting model Incorporating spatiotemporal information from multi-source data W. Liu et al.
151. Hybrid Deep Learning Time-Lagged Precipitation-Runoff Model T. Calvette et al.
152. Forecasting High-Flow Discharges in a Flashy Catchment Using Multiple Precipitation Estimates as Predictors in Machine Learning Models A. Zanchetta et al.
153. Physics-enhanced machine learning models for streamflow discharge forecasting Y. Zhao et al.
154. Quantifying uncertainties in data and model: a prediction model for extreme rainfall events with application to Beijing subway L. Mu et al.
155. Identifying trustworthiness challenges in deep learning models for continental-scale water quality prediction X. Xia et al.
156. Integrating geographic data and the SCS-CN method with LSTM networks for enhanced runoff forecasting in a complex mountain basin M. Merizalde et al.
157. Enhancing dissolved oxygen prediction through a hybrid attention-based LSTM model with wavelet transform and auto-encoder N. Shiri et al.
158. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al.
159. Instantaneous physical rainfall–runoff prediction technique using a power–law relationship between time to peak and peak flow of an instantaneous unit hydrograph and the rainfall excess intensity M. Jeong & D. Kim
160. The Great Catchment Hysteresis Challenge K. Beven et al.
161. Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al.
162. Enhanced prediction of river dissolved oxygen through feature- and model-based transfer learning X. Chen et al.
163. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al.
164. LandBench 1.0: A benchmark dataset and evaluation metrics for data-driven land surface variables prediction Q. Li et al.
165. Uncertainty and driving factor analysis of streamflow forecasting for closed-basin and interval-basin: Based on a probabilistic and interpretable deep learning model C. Xu et al.
166. Explainable deep learning-based simulation for evaluating climate-driven future groundwater level changes in South Korea J. Hwang & K. Lee
167. Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al.
168. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al.
169. Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction B. Demiray et al.
170. Hybrid-input ensemble neural network for improving flood-forecasting performance K. Watanabe et al.
171. Machine learning shows a limit to rain-snow partitioning accuracy when using near-surface meteorology K. Jennings et al.
172. Novel deep learning algorithm in soil erodibility factor predicting at a continental scale A. Shirzadi et al.
173. A comparative study of daily streamflow forecasting using firefly, artificial bee colony, and genetic algorithm-based artificial neural network H. Kilinc et al.
174. 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
175. Development of a Two-Stage LSTM for Multi-Step Runoff Forecasting Using a XAJ Model and EEMD Z. Yang et al.
176. From gauged to ungauged: Large-scale deep learning rainfall-runoff modelling for reliable streamflow estimation in India's diverse basins S. Barbhuiya & V. Gupta
177. Integration of Deep Learning Neural Networks and Feature-Extracted Approach for Estimating Future Regional Precipitation S. Lin et al.
178. Bayesian deep learning for probabilistic aquifer vulnerability and uncertainty prediction T. Mengistu et al.
179. Probabilistic Sub‐Daily Streamflow Forecasting in Data‐Scarce Catchments Through a Zero‐Shot Inductive Transfer Learning Approach E. Heidari & A. Khan
180. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al.
181. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al.
182. Machine learning for real-time reservoir operation simulation: comparing input variables and algorithms for the Sirikit Reservoir, Thailand C. Wannasin et al.
183. Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations J. Hammond et al.
184. Deep Learning–Based Streamflow Forecasting in a Snowmelt-Dominated Basin: A Regime-Aware Evaluation R. Vazirian
185. Deep Convolutional LSTM for improved flash flood prediction P. Oddo et al.
186. A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation B. Sun et al.
187. Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S. Y. Yang et al.
188. Hybrid forecasting: blending climate predictions with AI models L. Slater et al.
189. AI-enabled predictive pipelines for early warning of agricultural pests, plant diseases, and drought M. Sarker
190. 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.
191. Catchment features-based interpretation of performance of the conceptual hydrological and deep learning models using large sample hydrologic data D. Sourya et al.
192. Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling K. Ishida et al.
193. Predicting annual peak daily streamflow in natural basins using quantile regression forests K. Kim et al.
194. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al.
195. Revealing the causes of groundwater level dynamics in seasonally frozen soil zones using interpretable deep learning models H. Li et al.
196. Event-based training data thresholds for BiLSTM versus Xinanjiang models: Insights from the applications of 19 Chinese catchments Y. Li et al.