88 citations as recorded by crossref.

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2. How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions A. Sun & B. Scanlon 10.1088/1748-9326/ab1b7d
3. Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations S. Araya & T. Ghezzehei 10.1029/2018WR024357
4. The diagnosis about spatio-temporal characteristics and driving factors of flash drought and its prediction over typical humid and semi-arid basins in China Q. Zhu & Y. Wang 10.1175/JHM-D-21-0062.1
5. Visualization and ecohydrologic models: Opening the box C. Tague & J. Frew 10.1002/hyp.13991
6. Data-Driven Flood Alert System (FAS) Using Extreme Gradient Boosting (XGBoost) to Forecast Flood Stages W. Sanders et al. 10.3390/w14050747
7. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al. 10.5194/essd-13-5591-2021
8. Genetic programming for hydrological applications: to model or to forecast that is the question H. Herath et al. 10.2166/hydro.2021.179
9. Impact of deep learning-based dropout on shallow neural networks applied to stream temperature modelling A. Piotrowski et al. 10.1016/j.earscirev.2019.103076
10. Using machine learning models to predict and choose meshes reordered by graph algorithms to improve execution times for hydrological modeling L. Leonard 10.1016/j.envsoft.2019.03.023
11. A Regional Earth System Data Lab for Understanding Ecosystem Dynamics: An Example from Tropical South America L. Estupinan-Suarez et al. 10.3389/feart.2021.613395
12. 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
13. Integration of Multisource Data to Estimate Downward Longwave Radiation Based on Deep Neural Networks F. Zhu et al. 10.1109/TGRS.2021.3094321
14. Ecohydrology of Interannual Changes in Watershed Storage J. Rice & R. Emanuel 10.1029/2019WR025164
15. Neural network based process coupling and parameter upscaling in reactive transport simulations N. Prasianakis et al. 10.1016/j.gca.2020.07.019
16. Urban pluvial flooding prediction by machine learning approaches – a case study of Shenzhen city, China Q. Ke et al. 10.1016/j.advwatres.2020.103719
17. Artificial neural networks vis-à-vis MODFLOW in the simulation of groundwater: a review N. Zeydalinejad 10.1007/s40808-022-01365-y
18. Hydrological Early Warning System Based on a Deep Learning Runoff Model Coupled with a Meteorological Forecast A. de la Fuente et al. 10.3390/w11091808
19. Forecasting of water level in multiple temperate lakes using machine learning models S. Zhu et al. 10.1016/j.jhydrol.2020.124819
20. Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network Y. Zhou et al. 10.3390/w12020578
21. From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale? W. Zhi et al. 10.1021/acs.est.0c06783
22. Machine learning for hydrologic sciences: An introductory overview T. Xu & F. Liang 10.1002/wat2.1533
23. Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales D. Feng et al. 10.1029/2019WR026793
24. The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China M. Liu et al. 10.3390/w12020440
25. Multiorder Hydrologic Position in the Conterminous United States: A Set of Metrics in Support of Groundwater Mapping at Regional and National Scales K. Belitz et al. 10.1029/2019WR025908
26. From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling W. Tsai et al. 10.1038/s41467-021-26107-z
27. Streamflow prediction in ungauged basins: benchmarking the efficiency of deep learning G. Ayzel et al. 10.1051/e3sconf/202016301001
28. Evaluating the Potential and Challenges of an Uncertainty Quantification Method for Long Short‐Term Memory Models for Soil Moisture Predictions K. Fang et al. 10.1029/2020WR028095
29. A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network M. Sibtain et al. 10.1155/2020/8828664
30. Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes Z. Huang & T. Zhao 10.1002/wat2.1580
31. Deep Learning Framework with Time Series Analysis Methods for Runoff Prediction Z. Li et al. 10.3390/w13040575
32. 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
33. Adaptability of machine learning methods and hydrological models to discharge simulations in data-sparse glaciated watersheds H. Ji et al. 10.1007/s40333-021-0066-5
34. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
35. Deep learning in environmental remote sensing: Achievements and challenges Q. Yuan et al. 10.1016/j.rse.2020.111716
36. Simulating sediment discharge at water treatment plants under different land use scenarios using cascade modelling with an expert-based erosion-runoff model and a deep neural network E. Patault et al. 10.5194/hess-25-6223-2021
37. Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić 10.5194/hess-26-795-2022
38. Earth system data cubes unravel global multivariate dynamics M. Mahecha et al. 10.5194/esd-11-201-2020
39. Performance comparison of physical process-based and data-driven models: a case study on the Edwards Aquifer, USA A. Zhang et al. 10.1007/s10040-020-02169-z
40. River/stream water temperature forecasting using artificial intelligence models: a systematic review S. Zhu & A. Piotrowski 10.1007/s11600-020-00480-7
41. Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network D. Li et al. 10.1016/j.jhydrol.2021.126888
42. Two decades of ensemble flood forecasting: a state-of-the-art on past developments, present applications and future opportunities J. Das et al. 10.1080/02626667.2021.2023157
43. Exploring Copula-based Bayesian Model Averaging with multiple ANNs for PM2.5 ensemble forecasts Y. Zhou et al. 10.1016/j.jclepro.2020.121528
44. An improved long short-term memory network for streamflow forecasting in the upper Yangtze River S. Zhu et al. 10.1007/s00477-020-01766-4
45. Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. 10.5194/hess-25-4373-2021
46. Ensemble flood forecasting: Current status and future opportunities W. Wu et al. 10.1002/wat2.1432
47. Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links J. Pudashine et al. 10.1029/2019WR026255
48. Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization Z. Feng et al. 10.1016/j.jhydrol.2019.06.045
49. Distinguishing the relative impacts of climate change and anthropogenic activities on variation of water age in the Lake Poyang F. Hongxiang et al. 10.18307/2021.0419
50. Improving Precipitation Estimation Using Convolutional Neural Network B. Pan et al. 10.1029/2018WR024090
51. Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel K. Fang & C. Shen 10.1175/JHM-D-19-0169.1
52. Best Management Practices for Diffuse Nutrient Pollution: Wicked Problems Across Urban and Agricultural Watersheds A. Lintern et al. 10.1021/acs.est.9b07511
53. GRAINet: mapping grain size distributions in river beds from UAV images with convolutional neural networks N. Lang et al. 10.5194/hess-25-2567-2021
54. Hydrologic multi-model ensemble predictions using variational Bayesian deep learning D. Li et al. 10.1016/j.jhydrol.2021.127221
55. Velocity Field Estimation on Density‐Driven Solute Transport With a Convolutional Neural Network P. Kreyenberg et al. 10.1029/2019WR024833
56. An Attempt to Boost Posterior Population Expansion Using Fast Machine Learning Algorithms P. Juda & P. Renard 10.3389/frai.2021.624629
57. Deep Autoregressive Neural Networks for High‐Dimensional Inverse Problems in Groundwater Contaminant Source Identification S. Mo et al. 10.1029/2018WR024638
58. Simulator for Hydrologic Unstructured Domains (SHUD v1.0): numerical modeling of watershed hydrology with the finite volume method L. Shu et al. 10.5194/gmd-13-2743-2020
59. Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects O. Odebiri et al. 10.1007/s10661-021-09561-6
60. Stochastic simulation on reproducing long-term memory of hydroclimatological variables using deep learning model T. Lee et al. 10.1016/j.jhydrol.2019.124540
61. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks F. Kratzert et al. 10.5194/hess-22-6005-2018
62. From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering J. Hering 10.1061/(ASCE)EE.1943-7870.0001578
63. A framework for projecting future intensity-duration-frequency (IDF) curves based on CORDEX Southeast Asia multi-model simulations: An application for two cities in Southern Vietnam W. Zhao et al. 10.1016/j.jhydrol.2021.126461
64. Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models D. Li et al. 10.1029/2021WR029772
65. Advent of Big Data technology in environment and water management sector J. Gohil et al. 10.1007/s11356-021-14017-y
66. Lake water-level fluctuation forecasting using machine learning models: a systematic review S. Zhu et al. 10.1007/s11356-020-10917-7
67. Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks G. Ayzel et al. 10.3390/hydrology8010006
68. Unravelling groundwater time series patterns: Visual analytics-aided deep learning in the Namoi region of Australia S. Clark 10.1016/j.envsoft.2022.105295
69. GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models T. Gleeson et al. 10.5194/gmd-14-7545-2021
70. Toward catchment hydro‐biogeochemical theories L. Li et al. 10.1002/wat2.1495
71. Development of enthalpy-based climate indicators for characterizing building cooling and heating energy demand under climate change C. Tian et al. 10.1016/j.rser.2021.110799
72. What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al. 10.1029/2020WR028091
73. Input dropout in product unit neural networks for stream water temperature modelling A. Piotrowski et al. 10.1016/j.jhydrol.2021.126253
74. Advances in the Remote Sensing of Terrestrial Evaporation M. McCabe et al. 10.3390/rs11091138
75. Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization J. Zhang et al. 10.1029/2020WR027399
76. Challenges and opportunities in precision irrigation decision-support systems for center pivots J. Zhang et al. 10.1088/1748-9326/abe436
77. Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
78. Process‐Guided Deep Learning Predictions of Lake Water Temperature J. Read et al. 10.1029/2019WR024922
79. Machine learning based identification of dominant controls on runoff dynamics H. Oppel & A. Schumann 10.1002/hyp.13740
80. Revealing Causal Controls of Storage-Streamflow Relationships With a Data-Centric Bayesian Framework Combining Machine Learning and Process-Based Modeling W. Tsai et al. 10.3389/frwa.2020.583000
81. Editorial: Broadening the Use of Machine Learning in Hydrology C. Shen et al. 10.3389/frwa.2021.681023
82. Machine Learning Predicts Reach‐Scale Channel Types From Coarse‐Scale Geospatial Data in a Large River Basin H. Guillon et al. 10.1029/2019WR026691
83. A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk M. Rohde et al. 10.3389/feart.2021.784499
84. Prediction of flood quantiles at ungauged catchments for the contiguous USA using Artificial Neural Networks V. Filipova et al. 10.2166/nh.2021.082
85. GRQA: Global River Water Quality Archive H. Virro et al. 10.5194/essd-13-5483-2021
86. On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation T. Wagener et al. 10.1002/wat2.1550
87. Deep Learning as a Tool to Forecast Hydrologic Response for Landslide‐Prone Hillslopes E. Orland et al. 10.1029/2020GL088731
88. The Value of SMAP for Long-Term Soil Moisture Estimation With the Help of Deep Learning K. Fang et al. 10.1109/TGRS.2018.2872131