70 citations as recorded by crossref.

1. Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering R. Muñoz-Carpena et al.
2. A physics-driven hybrid transformer model for hydrologic simulation under nonstationary environmental conditions H. Zhang et al.
3. Multi-step ahead probabilistic runoff forecasting with SHAP interpretability: a GPR-enhanced deep learning ensemble approach integrating teleconnection factors F. Zhu et al.
4. Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations P. Egenlauf et al.
5. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al.
6. Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning F. Ghobadi et al.
7. The matrix-vector differential-form Muskingum method for river network routing J. Zhao et al.
8. 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
9. DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling A. Kapoor et al.
10. CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland B. Kraft et al.
11. A differentiability-based processes and parameters learning hydrologic model for advancing runoff prediction and process understanding C. Zhang et al.
12. Exploring Kolmogorov-Arnold neural networks for hybrid and transparent hydrological modeling X. Jing et al.
13. Improving Hydrological Simulations with a Dynamic Vegetation Parameter Framework H. Gu et al.
14. A fast physically-guided emulator of MATSIRO land surface model R. Olson et al.
15. Diverse methods of incorporating physics into neural networks: A comprehensive review M. Rajabi et al.
16. Towards generalized heap leaching modeling: A hybrid approach of phenomenological-principles and neural networks M. Bravo-Gutiérrez et al.
17. Integration of Satellite-Derived Meteorological Inputs into SWAT, XGBoost, WGAN, and Hybrid Modelling Frameworks for Climate Change-Driven Streamflow Simulation in a Data-Scarce Region S. Yeşilyurt & G. Onuşluel Gül
18. 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
19. Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil G. Costa et al.
20. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al.
21. Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al.
22. Establishing performance criteria for evaluating watershed-scale sediment and nutrient models at fine temporal scales A. Pandit et al.
23. Assessment of the Impact of Meteorological Variables on Lake Water Temperature Using the SHapley Additive exPlanations Method T. Amnuaylojaroen et al.
24. A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al.
25. A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl X. Jing et al.
26. Multi-step ahead streamflow forecasting method using Embedding Multi-Layer Perceptron Y. Li & S. Yang
27. A distributed hybrid physics–AI framework for learning corrections of internal hydrological fluxes and enhancing high-resolution regionalized flood modeling N. Huynh et al.
28. A comparative evaluation of streamflow prediction using the SWAT and NNAR models in the Meenachil River Basin of Central Kerala, India M. Saranya & V. Vinish
29. Fully differentiable, fully distributed rainfall-runoff modeling F. Scholz et al.
30. Selecting a conceptual hydrological model using Bayes' factors computed with replica-exchange Hamiltonian Monte Carlo and thermodynamic integration D. Mingo et al.
31. Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia) I. Leščešen et al.
32. Neural ordinary differential equations-based approach for enhanced building energy modeling on small datasets Z. Ma et al.
33. Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al.
34. A multi-step strategy for enhancing the rainfall-runoff modeling: combination of lumped and artificial intelligence-based hydrological models B. Mohammadi et al.
35. Learning landscape features from streamflow with autoencoders A. Bassi et al.
36. Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al.
37. A hybrid physics–AI approach using universal differential equations with state-dependent neural networks for learnable, regionalizable, spatially distributed hydrological modeling N. Huynh et al.
38. Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al.
39. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al.
40. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al.
41. Scientific machine learning in hydrology: a unified perspective A. Adombi
42. A comparative assessment of a hybrid approach against conventional and machine-learning daily streamflow prediction in ungauged basins S. Lee & D. Kim
43. Innovative daily runoff prediction model integrating black-winged kite algorithm and Mamba2–Transformer architecture D. Xu et al.
44. A hydrological knowledge-informed LSTM model for monthly streamflow reconstruction using distributed data: Application to typical rivers across the Tibetan plateau S. Hou et al.
45. Understanding the inter-event variability of recession flow characteristics and its drivers O. Rashid & T. Apurv
46. Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning M. He et al.
47. Integrating multi-task learning with a differentiable physics constrained framework for hydrological forecasting Y. Yan et al.
48. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al.
49. A guide to neural ordinary differential equations: Machine learning for data-driven digital engineering J. Worsham & J. Kalita
50. NuwaDynamics+: A Causality-Aware Generative Framework for Spatio-Temporal Representation Learning K. Wang et al.
51. Optimal Experimental Design for Universal Differential Equations C. Plate et al.
52. Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow V. Shankar et al.
53. When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al.
54. Parameter optimization method of hydrological model based on neural ordinary differential equations Q. Xiangzhao et al.
55. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al.
56. Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning R. Palmitessa et al.
57. Hybrid neutral network for daily streamflow projection on a sub-seasonal timescale E. Castro et al.
58. Hybrid hydrological modeling for large alpine basins: a semi-distributed approach B. Li et al.
59. Enhancing representation of data-scarce reservoir-regulated river basins using a hybrid DL-process based approach L. Deng et al.
60. Hybrid-GR4J: A hybrid hydrological model integrating GR4J and deep learning M. Sun et al.
61. A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al.
62. Physically Consistent Runoff Simulation in Mountainous Catchments Using a Time-Varying Gated Hybrid XAJ–LSTM Model H. Shen et al.
63. Deciphering the daily spatiotemporal dynamics and mechanisms of floods in the Tarim Basin desert region A. Tursun et al.
64. Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al.
65. Two-dimensional differential form of distributed Xinanjiang model J. Zhao et al.
66. Assessing the impact of land use and land cover on predicting in-stream total phosphorus using GIS and machine learning models H. Lee et al.
67. Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network C. Frank et al.
68. A review of machine learning methods for geopolymer performance prediction: Linking data-driven models with physical insights S. Zheng et al.
69. Deep learning for water quality W. Zhi et al.
70. Streamflow forecasting using machine learning for flood management and mitigation in the White Volta basin of Ghana J. Katsekpor et al.