156 citations as recorded by crossref.

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15. Streamflow Predictions in Ungauged Basins Using Recurrent Neural Network and Decision Tree-Based Algorithm: Application to the Southern Region of the Korean Peninsula J. Won et al.
16. Source Analysis of Groundwater Chemical Components in the Middle Reaches of the Dawen River Based on Unsupervised Machine Learning and PMF Source Analysis X. Wang et al.
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23. Value of process understanding in the era of machine learning: A case for recession flow prediction P. Istalkar et al.
24. Review of Watershed Hydrology and Mathematical Models S. Sarker & O. Leta
25. Bottom-up assessment of climate change vulnerability of a large and complex river basin using emulator models A. John et al.
26. Basin-Scale Streamflow Projections for Greater Pamba River Basin, India Integrating GCM Ensemble Modelling and Flow Accumulation-Weighted LULC Overlay in Deep Learning Environment A. Geetha Raveendran Nair et al.
27. Comparison of Multilayer Perceptron with an Optimal Activation Function and Long Short-Term Memory for Rainfall-Runoff Simulations and Ungauged Catchment Runoff Prediction M. Shin & Y. Jung
28. Using Entity-Aware LSTM to Enhance Streamflow Predictions in Transboundary and Large Lake Basins Y. Park et al.
29. Fusing dynamic physical constraints with PINN-xLSTM to enhance accuracy and physical consistency in runoff prediction under extreme hydrological events Y. Yang et al.
30. Evaluating Rainfall-Runoff Generation Mechanisms of Deep Learning Models Using a Process-Based Rainfall-Runoff Model T. Duong et al.
31. Hybrid Hydrological Forecasting Through a Physical Model and a Weather-Informed Transformer Model: A Case Study in Greek Watershed H. Ampas et al.
32. Enhancing Multi-Step Ahead Daily Runoff Prediction via HydMoE Model with Local-Global Hybrid Attention P. Yang & D. Chen
33. Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis J. Martel et al.
34. Coping with data scarcity in extreme flood forecasting: A deep generative modeling approach A. Sattari & H. Moradkhani
35. Assessing the Impact of Climate Change on an Ungauged Watershed in the Congo River Basin S. Masamba et al.
36. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al.
37. Leveraging Immersive Digital Twins and AI-Driven Decision Support Systems for Sustainable Water Reserves Management: A Conceptual Framework T. Zhao et al.
38. Enhancing streamflow predictions through basin-to-basin knowledge transfer: A novel strategy for deep learning models adaptation and generalization K. Nifa et al.
39. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al.
40. Deciphering the daily spatiotemporal dynamics and mechanisms of floods in the Tarim Basin desert region A. Tursun et al.
41. A GNN routing module is all you need for LSTM Rainfall–Runoff models H. Mosaffa et al.
42. Hydrological insights from a comparative evaluation of LSTM and MC-LSTM networks in the Ebro River basin (Spain) I. González-Planet & C. Juez
43. 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.
44. Improving Daily Streamflow Predictions over Large Watersheds: Introducing a Novel Enhanced Long Short-Term Memory (En-LSTM) Model P. Sharma et al.
45. HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network T. Nguyen et al.
46. AH-GCAN-LSTM: Adaptive Hybrid-Graph Convolution Attention Network with LSTM and Genetic Optimization for Hydrological time Series Forecasting H. Kilinc et al.
47. Hybrid GR4J-LSTM modeling for streamflow prediction of extreme events in data-scarce regions: Upper Blue Nile Basin, Ethiopia T. Mihret et al.
48. Application of distribution network monitoring information automatic verification platform driven by artificial intelligence in improving acceptance testing and power grid operation and maintenance management efficiency M. Jianwei et al.
49. Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al.
50. Strategies for incorporating static features into global deep learning models T. Liesch & M. Ohmer
51. Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes V. Tran et al.
52. Enhanced monthly streamflow prediction using an input–output bi-decomposition data driven model considering meteorological and climate information Q. Guo et al.
53. 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.
54. CNN-Based Subnetworks for Improved Modelling of Extreme Flood and Drought Events in the Negro River Basin C. Eleutério et al.
55. Harnessing the strengths of machine learning and geostatistics to improve streamflow prediction in ungauged basins; the best of both worlds V. Grey et al.
56. HydroGRAF: Hybrid discharge reconstruction and basin-aware streamflow forecasting in the Himalayas A. Gul et al.
57. Regional vs local LSTM models for short-term streamflow forecasting under operational constraints J. Saavedra-Garrido et al.
58. A comparative assessment of a hybrid approach against conventional and machine-learning daily streamflow prediction in ungauged basins S. Lee & D. Kim
59. Future projections of China runoff changes based on CMIP6 and deep learning X. Wei et al.
60. Enhancing large-scale streamflow prediction by coupling structural inductive bias with meta-learning X. Tang et al.
61. Uneven shifts in flood and drought flows in a Brazilian water supply catchment T. Mattos et al.
62. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al.
63. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven
64. Revisit hydrological modeling in ungauged catchments comparing regionalization, satellite observations, and machine learning approaches R. Dasgupta et al.
65. Enhancing physically based and distributed hydrological model calibration through internal state variable constraints F. Talbot et al.
66. A new method for predicting chlorophyll-a concentration in a reservoir: Coupling EFDC hydrodynamic and water quality model with ConvLSTM-MLP network H. Meng et al.
67. Analysis of data cleansing methods for improving meteorological data quality: a case study M. Rahman & M. Khan
68. Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al.
69. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al.
70. Development of The Navigator: A Lagrangian sensing system to characterize surface freshwater ecosystems A. Khandelwal et al.
71. A comparative study of long-term precipitation forecasting models based on CEEMDAN decomposition X. Lu & Y. Zhao
72. Metamodeling of a physically based pesticide runoff model with a long short-term memory approach G. Métayer et al.
73. 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.
74. Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins S. Wi et al.
75. Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation G. Oh
76. Satellite-based rainfall estimates to simulate daily streamflow using a hydrological model over Gambia watershed B. Faty et al.
77. Risk analysis and intelligent prediction of dam overtopping considering dynamic reservoir capacity Y. Chen et al.
78. Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources J. Willard et al.
79. Improving daily streamflow simulations for data-scarce watersheds using the coupled SWAT-LSTM approach S. Chen et al.
80. A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al.
81. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan
82. Application of Artificial Intelligence in Hydrological Modeling for Streamflow Prediction in Ungauged Watersheds: A Review J. Gacu et al.
83. Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments M. Farahani et al.
84. Effectiveness of three machine learning models for prediction of daily streamflow and uncertainty assessment L. Vinokić et al.
85. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn
86. Exploring the attributive impacts of climate and land use changes on streamflow in an urbanized watershed by deep learning and physically based modeling A. Geykli et al.
87. Improving multi-model ensemble streamflow forecasts by combining lumped, distributed and deep learning hydrological models W. Armstrong et al.
88. Interpretable deep learning hybrid streamflow prediction modeling based on multi-source data fusion Z. Wang et al.
89. Enhancing monthly runoff prediction in arid alpine basins of northwestern China by an EMD-PCA-LSTM hybrid model W. Hu et al.
90. 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.
91. Frequency-Domain Convolutional Network With Historical Data Fusion Module for Regional Streamflow Prediction Y. Zhang et al.
92. Assessing temporal and spatial generalization of LSTMs for streamflow modeling in French watersheds with and without European training data M. Puche et al.
93. An Easy-to-Apply Machine Learning Framework for Hydrologic Evaluation of Ungauged Catchments B. Baishya & A. Sarma
94. A novel hybrid machine learning model for shopping trip estimation: A case study of Tehran, Iran M. Dasoomi et al.
95. Integration of Soil Moisture and Meteorological Data Using Deep Learning for Flash Drought Detection in Northeastern Brazil I. Vásquez P. et al.
96. Neural network approach for modeling future natural river flows: Assessing climate change impacts on the Tagus River D. Fernández-Nóvoa et al.
97. Streamflow prediction in ungauged basins located within data-scarce areas using XGBoost: role of feature engineering and explainability M. Alipour
98. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al.
99. Non-Stationarity of Hydroclimatic Memory—Is Hydrological Memory Changing Under Climate Warming? M. Birylo
100. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al.
101. Leveraging neural network models to improve boundary condition inputs for the CE-QUAL-W2 model in reservoir turbidity simulations S. Kim & S. Chung
102. Advancing stream temperature prediction with a generalizable large-sample framework across CONUS river reaches M. Bakar Siddik et al.
103. Modeling runoff with incomplete data: a comparison of hydrological, deep learning, and hybrid approaches J. Wu et al.
104. Benchmarking process-based SWAT and customized LSTM rainfall-runoff models for the climate change impact assessment on the streamflow of the Upper Meghna River Basin K. Rahaman et al.
105. An explainable AI approach for interpreting regionally optimized deep neural networks in hydrological prediction F. Hosseini et al.
106. A framework on utilizing of publicly availability stream gauges datasets and deep learning in estimating monthly basin-scale runoff in ungauged regions M. Le et al.
107. Hydrology and Carbon Flux Interconnections in a Hemiboreal Forest: Impacts of Heatwaves in Järvselja, Estonia F. Civitate et al.
108. Spatiotemporal estimation of actual forest evapotranspiration in semi-arid China using remote sensing and deep learning R. Su et al.
109. Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso Y. Song & J. Zhang
110. A new integrated prediction method of river level based on spatiotemporal correlation Y. Li et al.
111. Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning M. He et al.
112. Utilizing machine learning to estimate monthly streamflow in ungauged basins of Thailand's southern basin N. Salaeh et al.
113. Hybrid neutral network for daily streamflow projection on a sub-seasonal timescale E. Castro et al.
114. A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al.
115. Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al.
116. Advancement of a Blended Hydrologic Model for Robust Model Performance R. Chlumsky et al.
117. 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.
118. An integrated strategy for evaluating flood susceptibility combining deep neural networks with biologically inspired meta-heuristic algorithms J. Hao et al.
119. Analysis of data splitting on streamflow prediction using random forest D. Puri et al.
120. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés
121. When are AI models ready for deployment? Reassessing Google’s global AI flood forecasting system through the lens of responsible modelling K. Li et al.
122. Improved streamflow prediction accuracy in Boreal climate watershed using a LSTM model: A comparative study K. Islam et al.
123. Integrating Convolutional Attention and Encoder–Decoder Long Short-Term Memory for Enhanced Soil Moisture Prediction J. Han et al.
124. Regionalizing hydrologic information for runoff predictions beyond continental boundaries using machine learning M. Fathi & A. Awadallah
125. 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.
126. A novel hybrid fine-tuning method for supercharging deep learning model development for hydrological prediction M. Jahangir et al.
127. Modeling Geometrically Nonlinear FG Plates: A Fast and Accurate Alternative to IGA Method Based on Deep Learning S. Li et al.
128. Utilizing Artificial Intelligence Techniques for a Long–Term Water Resource Assessment in the ShihMen Reservoir for Water Resource Allocation H. Lin et al.
129. Multi-parameter approaches for improved ensemble prediction accuracy in hydrology and water quality modeling S. Shin et al.
130. 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
131. Probabilistic Sub‐Daily Streamflow Forecasting in Data‐Scarce Catchments Through a Zero‐Shot Inductive Transfer Learning Approach E. Heidari & A. Khan
132. Improving Monthly Streamflow Prediction by Deep Learning Model With Physics‐Based Rules L. Ni et al.
133. Improved historical reconstruction of daily flows and annual maxima in gauged and ungauged basins J. Martel et al.
134. Evaluation of deep learning models applied to reservoir outflow prediction under extreme flood events W. Li et al.
135. Synergizing machine learning and modified physical models for hydrology modeling: A case study of modified SIMHYD and TANK models X. Lei et al.
136. Advancing Machine Learning-Based Streamflow Prediction Through Event Greedy Selection, Asymmetric Loss Function, and Rainfall Forecasting Uncertainty S. Tofighi et al.
137. Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique H. Huang et al.
138. Weekly streamflow forecasting of Rhine river based on machine learning approaches Z. Khozani et al.
139. Predicting annual peak daily streamflow in natural basins using quantile regression forests K. Kim et al.
140. Climate change impact assessment on a German lowland river using long short-term memory and conceptual hydrological models A. Ley et al.
141. Multi-Machine Learning Ensemble Regionalization of Hydrological Parameters for Enhancing Flood Prediction in Ungauged Mountainous Catchments K. Li et al.
142. Understanding the relationship between streamflow forecast skill and value across the western US P. Modi et al.
143. Impacts of the mega cascade reservoirs on riverine hydrothermal regimes based on deep learning M. Yu et al.
144. Transfer learning using the global Caravan dataset for developing a local river streamflow prediction model A. Alzhanov et al.
145. Daily runoff prediction for ungauged basins: Optimization of model transfer and selection of donor basins X. Song et al.
146. A hybrid hydrologic modelling framework with data-driven and conceptual reservoir operation schemes for reservoir impact assessment and predictions N. Dong et al.
147. A comprehensive approach to building a continuous hydrologic model with Soil Moisture Accounting using Earth Observation data V. Thomopoulou et al.
148. Does MC-LSTM model improve the reliability of streamflow prediction in human-influenced watersheds? G. Sahu et al.
149. Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma
150. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al.
151. An LSTM network for joint modeling of streamflow and hydropower generation for run-of-river plants T. Roksvåg et al.
152. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al.
153. Bridging data-driven and process-based approaches for hydrological modeling in the tropics: insights from the Kelani River Basin, Sri Lanka R. Makumbura et al.
154. Application of LSTM coupled models in runoff simulation and prediction: a review S. Li et al.
155. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al.
156. 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.