92 citations as recorded by crossref.

1. Evaluating Rainfall-Runoff Generation Mechanisms of Deep Learning Models Using a Process-Based Rainfall-Runoff Model T. Duong et al.
2. Machine Learning-Based Reconstructions of Historical Daily and Monthly Runoff for the Laurentian Great Lakes R. Gupta et al.
3. Bridging training–projection gaps in purely data-driven deep learning for runoff under climate change Y. Cen et al.
4. Event-based training data thresholds for BiLSTM versus Xinanjiang models: Insights from the applications of 19 Chinese catchments Y. Li et al.
5. A spatiotemporally differentiated hybrid hydrological modeling strategy with dynamically adaptive runoff generation modes X. Ma et al.
6. Regional scale simulations of daily suspended sediment concentration at gauged and ungauged rivers using deep learning A. Gupta & D. Feng
7. 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
8. Assessment of Potential Surface Runoff in Tulasi Watershed of Kolhapur Using NRSC-CN Method V. Pawar-Patil et al.
9. Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz
10. A nonstationary stochastic simulator for clustered regional hydroclimatic extremes to characterize compound flood risk A. Nayak et al.
11. Regional vs local LSTM models for short-term streamflow forecasting under operational constraints J. Saavedra-Garrido et al.
12. Local vs regional neural air pollution forecasting models M. Sangiorgio & G. Guariso
13. Predicting abnormality-guided multimodal linguistic semantics Arabic image captioning N. Aljojo et al.
14. HydroGRAF: Hybrid discharge reconstruction and basin-aware streamflow forecasting in the Himalayas A. Gul et al.
15. On the value of a history of hydrology and the establishment of a History of Hydrology Working Group K. Beven et al.
16. Machine learning in stream and river water temperature modeling: a review and metrics for evaluation C. Corona & T. Hogue
17. Characterizing the influence of remotely sensed wetland and lake water storage on discharge using LSTM models M. Vanderhoof et al.
18. An LSTM network for joint modeling of streamflow and hydropower generation for run-of-river plants T. Roksvåg et al.
19. A hybrid statistical-dynamical forecast of seasonal streamflow for a catchment in the Upper Columbia River basin in Canada T. Swift-LaPointe et al.
20. An interpretable machine learning approach for alkalinity reconstruction in the Mediterranean Sea T. Tonelli et al.
21. Better continental-scale streamflow predictions for Australia: LSTM as a land surface model post-processor and standalone hydrological model A. Shokri et al.
22. Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir
23. A novel hybrid fine-tuning method for supercharging deep learning model development for hydrological prediction M. Jahangir et al.
24. Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural Networks W. Jiang et al.
25. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al.
26. Defense and Security Mechanisms in the Internet of Things: A Review S. Szymoniak et al.
27. Riverine heatwaves are an emergent climate change risk A. van Hamel et al.
28. On generalization, language, interpretability and the future of geo-scientific machine learning H. Gupta
29. Interpretable feature incorporation machine-learning framework for flood magnitude estimation E. Ford et al.
30. Overview of Modern Technologies for Acquiring and Analysing Acoustic Information Based on AI and IoT S. Szymoniak & Ł. Kuczyński
31. Toward routing river water in land surface models with recurrent neural networks M. Lima et al.
32. Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al.
33. 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.
34. How well do process-based and data-driven hydrological models learn from limited discharge data? M. Staudinger et al.
35. From gauged to ungauged: Large-scale deep learning rainfall-runoff modelling for reliable streamflow estimation in India's diverse basins S. Barbhuiya & V. Gupta
36. Catchment features-based interpretation of performance of the conceptual hydrological and deep learning models using large sample hydrologic data D. Sourya et al.
37. Artificial intelligence with earth observations provides continuous streamflow data across varying wildfire recurrence and recovery scenarios S. Uddin et al.
38. Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al.
39. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al.
40. Differentiable parameter learning of reservoir operation modules Z. Chen & T. Zhao
41. A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al.
42. A python framework for differentiable hydrological modeling and research workflow automation W. Ouyang et al.
43. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al.
44. Detection and attribution of eco-hydrological alteration based on deep learning-driven gap-filled runoff in a large-scale catchment Z. Dong et al.
45. 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.
46. Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany S. Kunz et al.
47. HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network T. Nguyen et al.
48. Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al.
49. Strategies for incorporating static features into global deep learning models T. Liesch & M. Ohmer
50. Application of Artificial Intelligence in Hydrological Modeling for Streamflow Prediction in Ungauged Watersheds: A Review J. Gacu et al.
51. Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al.
52. Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins S. Wi et al.
53. Enhancing monthly runoff prediction in arid alpine basins of northwestern China by an EMD-PCA-LSTM hybrid model W. Hu et al.
54. Monte-Carlo-assisted endo-exo temporal transformer for high-confidence interval forecasting of daily runoff X. Hu et al.
55. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan
56. Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis J. Martel et al.
57. Multi-site deep learning for groundwater level prediction across global datasets: toward scalable applications under data scarcity A. Nolte et al.
58. Bottom-up assessment of climate change vulnerability of a large and complex river basin using emulator models A. John et al.
59. An explainable AI approach for interpreting regionally optimized deep neural networks in hydrological prediction F. Hosseini et al.
60. How to deal w___ missing input data M. Gauch et al.
61. Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments M. Farahani et al.
62. A Comprehensive Calibration Framework for the Northwest River Forecast Center G. Walters et al.
63. Transfer learning using the global Caravan dataset for developing a local river streamflow prediction model A. Alzhanov et al.
64. Learning to filter: snow data assimilation using a Long Short-Term Memory network G. Blandini et al.
65. Near-Real-Time Statistical Analysis and Visualization of Streamflow from a Deep-Learning Rainfall-Runoff Model T. Duong et al.
66. A differentiability-based processes and parameters learning hydrologic model for advancing runoff prediction and process understanding C. Zhang et al.
67. CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland B. Kraft et al.
68. Time fractional Saint Venant equations reveal the physical basis of hydrograph retardation through model comparison and field data H. Wei et al.
69. Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review I. Malashin et al.
70. DeepDiscover: towards autonomous discovery of bucket-type conceptual models – a proof of concept applied to hydrology A. Adombi
71. Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features M. Gomez et al.
72. Future projections of China runoff changes based on CMIP6 and deep learning X. Wei et al.
73. When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al.
74. Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics J. Yang et al.
75. Are LSTM and conceptual rainfall-runoff models able to cope with limited training datasets under diverse hydrometeorological conditions? F. Boodoo et al.
76. Improving annual streamflow estimates using Budyko parameterization driven by catchment attributes H. Tamiru et al.
77. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al.
78. A runoff prediction method for arid regions integrating physics-guided signal extraction and temporally adaptive feature selection Z. Li et al.
79. Detecting differentiated services code point values and packet length mismatch in internet protocol packet headers M. A. Aldhahery
80. Exploring a process-aware spatiotemporal graph-based surrogate for integrated urban drainage simulation B. Yin et al.
81. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al.
82. Analysis of Baseline and Novel Boosting Models for Flood-Prone Prediction and Explainability: Case from the Upper Drâa Basin (Morocco) L. Goumghar et al.
83. Riverine heat waves on the rise, outpacing air heat waves K. Sadayappan & L. Li
84. Abnormal behavior modeling and intelligent error prevention algorithm in AI load forecasting S. Zhang & Q. Han
85. Hydrological insights from a comparative evaluation of LSTM and MC-LSTM networks in the Ebro River basin (Spain) I. González-Planet & C. Juez
86. Streamflow Forecasting: A Comparative Analysis of ARIMAX, Rolling Forecasting LSTM Neural Network and Physically Based Models in a Pristine Catchment D. Perazzolo et al.
87. A quantum-inspired attention integrated scalar long short-term memory model for accurate and stable groundwater contaminant source inversion L. Zhu & W. Lu
88. Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell E. Acuña Espinoza et al.
89. An Enhanced Hybrid LSTM–Linear Regression Framework for 90-Day Rainfall Forecasting in Rainfed Agricultural Regions A. Kunlerd et al.
90. Distilling hydrological and land-surface model parameters from physio-geographical properties using text-generating AI M. Feigl et al.
91. IoT-enabled real-time health monitoring system for adolescent physical rehabilitation J. Yang et al.
92. A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation B. Sun et al.