131 citations as recorded by crossref.

1. rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al. 10.21105/joss.04237
2. Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis Q. Tian et al. 10.3390/w15183184
3. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
4. Impact of spatial distribution information of rainfall in runoff simulation using deep learning method Y. Wang & H. Karimi 10.5194/hess-26-2387-2022
5. Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model A. Ley et al. 10.2166/nh.2024.003
6. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al. 10.5194/hess-27-139-2023
7. AI‐based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada P. Parisouj et al. 10.1111/1752-1688.13098
8. Prediction of mine water quality by the Seq2Seq model based on attention mechanism X. Wang & Y. Li 10.1016/j.heliyon.2024.e37916
9. Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić 10.3389/frwa.2022.934709
10. Operational low-flow forecasting using LSTMs J. Deng et al. 10.3389/frwa.2023.1332678
11. Green roofs and their effect on architectural design and urban ecology using deep learning approaches C. Wang et al. 10.1007/s00500-024-09637-8
12. Real-time monitoring and optimization of machine learning intelligent control system in power data modeling technology Q. Wang et al. 10.1016/j.mlwa.2024.100584
13. Modeling and Forecasting of nanoFeCu Treated Sewage Quality Using Recurrent Neural Network (RNN) D. Cao et al. 10.3390/computation11020039
14. Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics J. Frame et al. 10.1111/1752-1688.12964
15. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. 10.1029/2021WR029583
16. Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods Y. Yang & T. Chui 10.5194/hess-25-5839-2021
17. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al. 10.21105/joss.04050
18. Flood forecasting with machine learning models in an operational framework S. Nevo et al. 10.5194/hess-26-4013-2022
19. An advanced approach for the precise prediction of water quality using a discrete hidden markov model D. Li et al. 10.1016/j.jhydrol.2022.127659
20. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
21. A Case Study: Groundwater Level Forecasting of the Gyorae Area in Actual Practice on Jeju Island Using Deep-Learning Technique D. Kim et al. 10.3390/w15050972
22. Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP H. Xu et al. 10.3390/w15152760
23. Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
24. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al. 10.1002/hyp.14847
25. Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments P. Bhasme & U. Bhatia 10.1016/j.jhydrol.2023.130421
26. Runoff simulation driven by multi-source satellite data based on hydrological mechanism algorithm and deep learning network C. Yu et al. 10.1016/j.ejrh.2024.101720
27. A Runoff Prediction Model Based on Nonhomogeneous Markov Chain W. Li et al. 10.1007/s11269-022-03091-7
28. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi 10.1016/j.jhydrol.2024.131835
29. Deep transfer learning based on transformer for flood forecasting in data-sparse basins Y. Xu et al. 10.1016/j.jhydrol.2023.129956
30. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan 10.1029/2022WR033091
31. Predicting daily solar radiation using a novel hybrid long short-term memory network across four climate regions of China L. Xing et al. 10.1016/j.compag.2023.108139
32. Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al. 10.5194/npg-31-535-2024
33. Green Roof Hydrological Modelling With GRU and LSTM Networks H. Xie et al. 10.1007/s11269-022-03076-6
34. A Hybrid Model Combining the Cama-Flood Model and Deep Learning Methods for Streamflow Prediction M. Zhong et al. 10.1007/s11269-023-03583-0
35. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
36. Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model J. Aerts et al. 10.5194/hess-26-4407-2022
37. EWT_Informer: a novel satellite-derived rainfall–runoff model based on informer S. Wang et al. 10.2166/hydro.2023.228
38. Mind the information gap: How sampling and clustering impact the predictability of reach‐scale channel types in California (USA) H. Guillon et al. 10.1002/esp.5984
39. Stream water quality optimized prediction based on human activity intensity and landscape metrics with regional heterogeneity in Taihu Basin, China Y. Wang et al. 10.1007/s11356-022-22536-5
40. Temporal changes in precipitation and correlation with large climate indicators in the Hengshao Drought Corridor, China T. Zhang et al. 10.1016/j.ecolind.2024.112715
41. Mining relevant partial periodic pattern of multi-source time series data Y. Xun et al. 10.1016/j.ins.2022.10.049
42. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya T. Lees et al. 10.3390/rs14030698
43. Causality-Structured Deep Learning for Soil Moisture Predictions L. Li et al. 10.1175/JHM-D-21-0206.1
44. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider 10.1029/2022WR032123
45. High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. 10.1038/s41597-022-01785-6
46. Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study H. Yang & W. Li 10.1007/s11269-022-03389-6
47. Runoff Forecast Model Based on an EEMD-ANN and Meteorological Factors Using a Multicore Parallel Algorithm S. Liao et al. 10.1007/s11269-023-03442-y
48. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
49. Forecasting Multi-Step Soil Moisture with Three-Phase Hybrid Wavelet-Least Absolute Shrinkage Selection Operator-Long Short-Term Memory Network (moDWT-Lasso-LSTM) Model W. Jayasinghe et al. 10.3390/w16213133
50. Benchmarking data-driven rainfall-runoff modeling across 54 catchments in the Yellow River Basin: Overfitting, calibration length, dry frequency J. Jin et al. 10.1016/j.ejrh.2022.101119
51. 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. 10.1007/s11356-023-29522-5
52. 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 10.5194/hess-28-479-2024
53. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
54. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn 10.1016/j.jenvman.2024.122114
55. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River J. Xiong et al. 10.3390/rs13122272
56. 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. 10.5194/hess-25-5517-2021
57. Research on Annual Runoff Prediction Model Based on Adaptive Particle Swarm Optimization–Long Short-Term Memory with Coupled Variational Mode Decomposition and Spectral Clustering Reconstruction X. Wang et al. 10.3390/w16081179
58. Using explainable artificial intelligence (XAI) methods to understand the nonlinear relationship between the Three Gorges Dam and downstream flood X. Wei et al. 10.1016/j.ejrh.2024.101776
59. Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin C. Yang et al. 10.1016/j.jhydrol.2023.129990
60. Earth and Planetary Surface Processes Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science C. Burberry et al. 10.1029/2022EA002414
61. Scalable big earth observation data mining algorithms: a review N. Sisodiya et al. 10.1007/s12145-023-01032-5
62. Streamflow forecast model using ANN D. Sonowal et al. 10.1080/23249676.2024.2310098
63. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. 10.5194/hess-26-5449-2022
64. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
65. A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction B. Sahoo et al. 10.1007/s11269-023-03552-7
66. Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić 10.5194/hess-26-795-2022
67. Assessing multi-year-drought vulnerability in dense Mediterranean-climate forests using water-balance-based indicators G. Cui et al. 10.1016/j.jhydrol.2022.127431
68. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. 10.5194/hess-28-4187-2024
69. Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning Ž. Brkić & M. Kuhta 10.3390/su141610447
70. Simulating the climate driven runoff in data-scarce mountains by machine learning and downscaling reanalysis data M. Fan et al. 10.1007/s00477-022-02231-0
71. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
72. Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework L. Zhang et al. 10.3390/atmos15020164
73. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al. 10.5194/hess-28-2107-2024
74. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132269
75. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al. 10.3390/w16101407
76. Streamflow Prediction with Time-Lag-Informed Random Forest and Its Performance Compared to SWAT in Diverse Catchments D. Moges et al. 10.3390/w16192805
77. Statistical learning of water budget outcomes accounting for target and feature uncertainty N. Martin & C. Yang 10.1016/j.jhydrol.2023.129946
78. Intercomparing LSTM and RNN to a Conceptual Hydrological Model for a Low-Land River with a Focus on the Flow Duration Curve A. Ley et al. 10.3390/w15030505
79. Effects of mass balance, energy balance, and storage-discharge constraints on LSTM for streamflow prediction S. Pokharel et al. 10.1016/j.envsoft.2023.105730
80. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al. 10.1029/2022WR033918
81. A New Rainfall-Runoff Model Using Improved LSTM with Attentive Long and Short Lag-Time X. Chen et al. 10.3390/w14050697
82. Simulated annealing coupled with a Naïve Bayes model and base flow separation for streamflow simulation in a snow dominated basin H. Tongal & M. Booij 10.1007/s00477-022-02276-1
83. Estimating soil mineral nitrogen from data-sparse field experiments using crop model-guided deep learning approach R. Gupta et al. 10.1016/j.compag.2024.109355
84. Development and Applicability Assessment of an Hourly Water Level Prediction Model for Agricultural Reservoirs Using Automated Machine Learning J. Choi et al. 10.4491/KSEE.2024.46.10.602
85. Study on the Optimization and Stability of Machine Learning Runoff Prediction Models in the Karst Area C. Mo et al. 10.3390/app12104979
86. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
87. LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China J. Li et al. 10.3390/w14111745
88. Evaluation of Transformer model and Self-Attention mechanism in the Yangtze River basin runoff prediction X. Wei et al. 10.1016/j.ejrh.2023.101438
89. Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation W. Li et al. 10.1038/s41598-024-62127-7
90. Classification of imbalanced protein sequences with deep-learning approaches; application on influenza A imbalanced virus classes R. Ahsan et al. 10.1016/j.imu.2022.100860
91. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
92. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al. 10.1029/2021WR031065
93. How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models? R. Hashemi et al. 10.5194/hess-26-5793-2022
94. Spatio-temporal cross-validation to predict pluvial flood events in the Metropolitan City of Venice Z. Marco et al. 10.1016/j.jhydrol.2022.128150
95. Prediction of groundwater drought based on hydro-meteorological insights via machine learning approaches V. Kartal et al. 10.1016/j.pce.2024.103757
96. Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach A. Alexander & D. Kumar 10.1016/j.advwatres.2024.104842
97. Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting A. Amini et al. 10.1029/2022WR032789
98. A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir P. Nguyen et al. 10.1002/rvr2.72
99. Large-scale seasonal forecasts of river discharge by coupling local and global datasets with a stacked neural network: Case for the Loire River system M. Vu et al. 10.1016/j.scitotenv.2023.165494
100. Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. 10.1016/j.jhydrol.2022.128835
101. Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand P. Panyadee & P. Champrasert 10.3390/su16114433
102. Correction of Reservoir Runoff Forecast Based on Multi-scenario Division and Multi Models S. Wang et al. 10.1007/s11269-022-03305-y
103. Valuable Knowledge Mining: Deep Analysis of Heart Disease and Psychological Causes Based on Large-Scale Medical Data L. Wang et al. 10.3390/app132011151
104. Improving the streamflow prediction accuracy in sparse data regions: a fresh perspective on integrated hydrological-hydrodynamic and hybrid machine learning models S. Khorram & N. Jehbez 10.1080/19942060.2024.2387051
105. Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al. 10.1016/j.jhydrol.2024.131986
106. Prediction of storm surge in the Pearl River Estuary based on data-driven model Q. Tian et al. 10.3389/fmars.2024.1390364
107. Runoff Estimation in the Upper Reaches of the Heihe River Using an LSTM Model with Remote Sensing Data H. Xue et al. 10.3390/rs14102488
108. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. 10.5194/hess-26-5493-2022
109. Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media X. Zeng & Z. Zhong 10.4018/JOEUC.314786
110. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al. 10.5194/hess-26-3079-2022
111. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
112. A precipitation-runoff swift simulation model dedicated to emergency response to flood prediction Z. Yu et al. 10.1007/s12665-022-10604-5
113. Long-term inflow forecast using meteorological data based on long short-term memory neural networks H. Zhao et al. 10.2166/hydro.2024.196
114. Effect of Gradient Descent Optimizers and Dropout Technique on Deep Learning LSTM Performance in Rainfall-runoff Modeling D. Anh et al. 10.1007/s11269-022-03393-w
115. Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework L. Nguyen et al. 10.3390/w14030300
116. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al. 10.1029/2021WR030993
117. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al. 10.1002/hyp.14837
118. Probability of rainstorm and flood disasters due to extreme precipitation in Fen River Basin, China Y. Li et al. 10.1007/s11356-023-27351-0
119. Development of objective function-based ensemble model for streamflow forecasts Y. Lin et al. 10.1016/j.jhydrol.2024.130861
120. A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM B. Zhang et al. 10.32604/jrm.2022.021845
121. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
122. When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al. 10.5194/hess-28-3051-2024
123. The construction of urban cultural and creative industries using deep learning and information management Z. Zhao et al. 10.1016/j.heliyon.2024.e33787
124. A Comprehensive Assessment of the Hydrological Evolution and Habitat Quality of the Xiangjiang River Basin F. Hong et al. 10.3390/w15203626
125. Deep Multi-Stage Reference Evapotranspiration Forecasting Model: Multivariate Empirical Mode Decomposition Integrated With the Boruta-Random Forest Algorithm W. Jayasinghe et al. 10.1109/ACCESS.2021.3135362
126. Real-time rainfall and runoff prediction by integrating BC-MODWT and automatically-tuned DNNs: Comparing different deep learning models A. Amini et al. 10.1016/j.jhydrol.2024.130804
127. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn 10.1016/j.jhydrol.2024.130862
128. Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden C. Brenner et al. 10.1007/s00506-021-00768-y
129. The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records D. de Oliveira & J. Vrugt 10.1029/2022WR032263
130. Potential of hybrid wavelet-coupled data-driven-based algorithms for daily runoff prediction in complex river basins T. Bajirao et al. 10.1007/s00704-021-03681-2
131. 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. 10.5194/hess-25-1671-2021