77 citations as recorded by crossref.

1. Probabilistic multi-step ahead streamflow forecast based on deep learning D. Karimanzira et al.
2. Uncertainty Forecasting Model for Mountain Flood Based on Bayesian Deep Learning S. Wang & O. Xu
3. Runoff Estimation in the Upper Reaches of the Heihe River Using an LSTM Model with Remote Sensing Data H. Xue et al.
4. Hybrid forecasting: blending climate predictions with AI models L. Slater et al.
5. Evaluating Earth observation products for Catchment-Scale operational flood monitoring and risk management in a sparsely gauged to ungauged river basin in Nigeria D. Idowu et al.
6. Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments P. Bhasme & U. Bhatia
7. Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method Y. Lin et al.
8. Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicability C. Song
9. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider
10. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al.
11. Deciphering the daily spatiotemporal dynamics and mechanisms of floods in the Tarim Basin desert region A. Tursun et al.
12. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al.
13. Sensitivity of hydrological machine learning prediction accuracy to information quantity and quality M. Jeung et al.
14. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al.
15. A novel feature attention mechanism for improving the accuracy and robustness of runoff forecasting H. Wang et al.
16. The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment D. Feng et al.
17. Enhancing monthly runoff prediction in arid alpine basins of northwestern China by an EMD-PCA-LSTM hybrid model W. Hu et al.
18. Enhancing Multi-Step Reservoir Inflow Forecasting: A Time-Variant Encoder–Decoder Approach M. Fan et al.
19. Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins S. Wi et al.
20. Ensembling differentiable process-based and data-driven models with diverse meteorological forcing datasets to advance streamflow simulation P. Li et al.
21. rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al.
22. Enhancing groundwater level prediction accuracy at a daily scale through combined machine learning and physics-based modeling K. Sun et al.
23. How to deal w___ missing input data M. Gauch et al.
24. Deep learning rainfall–runoff predictions of extreme events J. Frame et al.
25. Two novel error-updating model frameworks for short-to-medium range streamflow forecasting using bias-corrected rainfall inputs: Development and comparative assessment A. Khatun et al.
26. On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process J. Frame et al.
27. Pre-processing satellite rainfall products improves hydrological simulations with machine learning T. Boulmaiz et al.
28. CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al.
29. Hybrid Deep Learning Time-Lagged Precipitation-Runoff Model T. Calvette et al.
30. Riverine heatwaves are an emergent climate change risk A. van Hamel et al.
31. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al.
32. Heterogeneous impacts of climate change on streamflow in typical watersheds of three mountain systems in Xinjiang, Northwest China C. Zan et al.
33. Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al.
34. Urban flood risk analysis using presence-only machine learning approach: an integrated MaxEnt-cloud model framework in Harbin, China J. Hu et al.
35. 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.
36. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al.
37. Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network C. Frank et al.
38. The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses P. Shuai et al.
39. From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction J. Liu et al.
40. Enhancing streamflow simulation in large and human-regulated basins: Long short-term memory with multiscale attributes A. Tursun et al.
41. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al.
42. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al.
43. Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir
44. 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
45. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al.
46. A hybrid Capsule-Transformer Network for daily runoff forecasting Z. Wu & H. Yan
47. Use of regional sensitivity analysis for diagnosing parsimony of models: A water model case study R. Srikanthan et al.
48. Temporal Fusion Transformers for streamflow Prediction: Value of combining attention with recurrence S. Rasiya Koya & T. Roy
49. 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.
50. Improving differentiable hydrologic modeling with interpretable forcing fusion K. Sawadekar et al.
51. Development of objective function-based ensemble model for streamflow forecasts Y. Lin et al.
52. Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al.
53. Adjusting rainfall inputs to hydrological models in a data-scarce area of Southern Africa D. Hughes
54. Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz
55. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al.
56. Daily Runoff Series Prediction Using GWO Optimization and Secondary Decomposition: A Case Study of the Xujiang River Basin Q. Li et al.
57. 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.
58. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al.
59. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn
60. Ensemble streamflow forecasting with diverse loss functions K. Dahal et al.
61. A comparative assessment of a hybrid approach against conventional and machine-learning daily streamflow prediction in ungauged basins S. Lee & D. Kim
62. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al.
63. Unveiling the Role of Weighted Loss Functions in Deep Learning-Based Nowcasting of Extreme Rainfall Events H. Choi et al.
64. Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis J. Martel et al.
65. Probing the limit of hydrologic predictability with the Transformer network J. Liu et al.
66. Global Tropical Cyclone Precipitation Estimation via a Multitask Convolutional Neural Network Based on HURSAT-B1 Data M. Xue et al.
67. Explainable global wildfire prediction model using graph neural networks D. Chen et al.
68. Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method X. Xiang et al.
69. Impact of Three Gorges Reservoir Operation on Water Level at Jiujiang Station and Poyang Lake in the Yangtze River Y. Wang et al.
70. Deep learning-based prediction of flood hazard and future streamflow changes in the Brahmaputra River Basin under CMIP6 climate change scenarios S. Shahariar et al.
71. Learning landscape features from streamflow with autoencoders A. Bassi et al.
72. Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al.
73. 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.
74. Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al.
75. Regionalization of a Distributed Hydrology Model Using Random Forest S. Markhali et al.
76. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al.
77. Future projections of China runoff changes based on CMIP6 and deep learning X. Wei et al.