159 citations as recorded by crossref.

1. High temporal resolution urban flood prediction using attention-based LSTM models L. Zhang et al. 10.1016/j.jhydrol.2023.129499
2. The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment D. Feng et al. 10.5194/hess-27-2357-2023
3. Visualization and ecohydrologic models: Opening the box C. Tague & J. Frew 10.1002/hyp.13991
4. Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China Q. Zhu et al. 10.5194/hess-28-1665-2024
5. CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al. 10.5194/essd-13-5591-2021
6. Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
7. Genetic programming for hydrological applications: to model or to forecast that is the question H. Herath et al. 10.2166/hydro.2021.179
8. Efficient analysis of hydrological connectivity using 1D and 2D Convolutional Neural Networks C. Nguyen et al. 10.1016/j.advwatres.2023.104583
9. 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
10. 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
11. Disentangling coastal groundwater level dynamics in a global dataset A. Nolte et al. 10.5194/hess-28-1215-2024
12. Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine M. Kim et al. 10.1029/2022WR032690
13. FlowDyn: A daily streamflow prediction pipeline for dynamical deep neural network applications S. Sadeghi Tabas et al. 10.1016/j.envsoft.2023.105854
14. Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions K. Tripathy & A. Mishra 10.1016/j.jhydrol.2023.130458
15. Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective S. Razavi et al. 10.1002/hyp.14596
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 Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science B. Acharya et al. 10.1029/2022EA002320
19. 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
20. Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration Y. Khoshkalam et al. 10.1016/j.jhydrol.2023.129682
21. A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations D. Aboelyazeed et al. 10.5194/bg-20-2671-2023
22. 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
23. 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
24. Spatiotemporal variations and overflow risk analysis of the Salt Lake in the Hoh Xil Region using machine learning methods R. Wang et al. 10.3389/feart.2022.1084540
25. 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
26. Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes Z. Huang & T. Zhao 10.1002/wat2.1580
27. A Data-Driven Framework for Spatiotemporal Analysis and Prediction of River Water Quality: A Case Study in Pearl River, China M. Lv et al. 10.3390/w15020257
28. Deep Learning Framework with Time Series Analysis Methods for Runoff Prediction Z. Li et al. 10.3390/w13040575
29. 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
30. A framework on utilizing of publicly availability stream gauges datasets and deep learning in estimating monthly basin-scale runoff in ungauged regions M. Le et al. 10.1016/j.advwatres.2024.104694
31. River/stream water temperature forecasting using artificial intelligence models: a systematic review S. Zhu & A. Piotrowski 10.1007/s11600-020-00480-7
32. Exploring Copula-based Bayesian Model Averaging with multiple ANNs for PM2.5 ensemble forecasts Y. Zhou et al. 10.1016/j.jclepro.2020.121528
33. Deep learning for photovoltaic panels segmentation K. Bouzaachane et al. 10.23939/mmc2023.03.638
34. Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. 10.1016/j.jhydrol.2022.128835
35. Ensemble flood forecasting: Current status and future opportunities W. Wu et al. 10.1002/wat2.1432
36. HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists R. Rigon et al. 10.5194/hess-26-4773-2022
37. 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
38. 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
39. Hydrologic multi-model ensemble predictions using variational Bayesian deep learning D. Li et al. 10.1016/j.jhydrol.2021.127221
40. 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
41. Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions R. Graf & V. Vyshnevskyi 10.3390/resources11120111
42. Velocity Field Estimation on Density‐Driven Solute Transport With a Convolutional Neural Network P. Kreyenberg et al. 10.1029/2019WR024833
43. An Attempt to Boost Posterior Population Expansion Using Fast Machine Learning Algorithms P. Juda & P. Renard 10.3389/frai.2021.624629
44. Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
45. Deep Autoregressive Neural Networks for High‐Dimensional Inverse Problems in Groundwater Contaminant Source Identification S. Mo et al. 10.1029/2018WR024638
46. 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
47. Text mining-aided meta-research on nutrient dynamics in surface water and groundwater: Popular topics and perceived gaps A. Elsayed et al. 10.1016/j.jhydrol.2023.130338
48. Stochastic simulation on reproducing long-term memory of hydroclimatological variables using deep learning model T. Lee et al. 10.1016/j.jhydrol.2019.124540
49. Progressive growing generative adversarial networks using conditioning ratio for facies modeling in complex aquifers F. Redoloza et al. 10.1007/s10040-023-02687-6
50. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks F. Kratzert et al. 10.5194/hess-22-6005-2018
51. From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering J. Hering 10.1061/(ASCE)EE.1943-7870.0001578
52. Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism L. Girihagama et al. 10.1007/s00521-022-07523-8
53. Improving the forecasting accuracy of monthly runoff time series of the Brahmani River in India using a hybrid deep learning model S. Swagatika et al. 10.2166/wcc.2023.487
54. Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction J. Choi et al. 10.3390/w14182910
55. The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management V. Kumar et al. 10.3390/su151310543
56. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al. 10.1029/2021WR030993
57. Unravelling groundwater time series patterns: Visual analytics-aided deep learning in the Namoi region of Australia S. Clark 10.1016/j.envsoft.2022.105295
58. 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
59. A systematic review on machine learning algorithms used for forecasting lake‐water level fluctuations S. Sannasi Chakravarthy et al. 10.1002/cpe.7231
60. Toward catchment hydro‐biogeochemical theories L. Li et al. 10.1002/wat2.1495
61. 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
62. MODIS Evapotranspiration Downscaling Using a Deep Neural Network Trained Using Landsat 8 Reflectance and Temperature Data X. Che et al. 10.3390/rs14225876
63. What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al. 10.1029/2020WR028091
64. Input dropout in product unit neural networks for stream water temperature modelling A. Piotrowski et al. 10.1016/j.jhydrol.2021.126253
65. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al. 10.3390/w16101407
66. Challenges and opportunities in precision irrigation decision-support systems for center pivots J. Zhang et al. 10.1088/1748-9326/abe436
67. Short-Term and Long-Term Replenishment of Water Storage Influenced by Lockdown and Policy Measures in Drought-Prone Regions of Central India S. Bhanja & M. Sekhar 10.3390/rs14081768
68. 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
69. Enhancing Water Temperature Prediction in Stratified Reservoirs: A Process-Guided Deep Learning Approach S. Kim & S. Chung 10.3390/w15173096
70. Editorial: Broadening the Use of Machine Learning in Hydrology C. Shen et al. 10.3389/frwa.2021.681023
71. A review of the application of machine learning in water quality evaluation M. Zhu et al. 10.1016/j.eehl.2022.06.001
72. Prediction of flood quantiles at ungauged catchments for the contiguous USA using Artificial Neural Networks V. Filipova et al. 10.2166/nh.2021.082
73. GRQA: Global River Water Quality Archive H. Virro et al. 10.5194/essd-13-5483-2021
74. Mapping Drainage Ditches in Forested Landscapes Using Deep Learning and Aerial Laser Scanning W. Lidberg et al. 10.1061/JIDEDH.IRENG-9796
75. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. 10.1029/2021WR030138
76. Deep Learning as a Tool to Forecast Hydrologic Response for Landslide‐Prone Hillslopes E. Orland et al. 10.1029/2020GL088731
77. Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins G. Saha et al. 10.1016/j.jenvman.2024.120721
78. Modelling of the shallow water table at high spatial resolution using random forests J. Koch et al. 10.5194/hess-23-4603-2019
79. Short- and mid-term forecasts of actual evapotranspiration with deep learning E. Babaeian et al. 10.1016/j.jhydrol.2022.128078
80. 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
81. Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations S. Araya & T. Ghezzehei 10.1029/2018WR024357
82. 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
83. Data-Driven Flood Alert System (FAS) Using Extreme Gradient Boosting (XGBoost) to Forecast Flood Stages W. Sanders et al. 10.3390/w14050747
84. Rainfall-driven machine learning models for accurate flood inundation mapping in Karachi, Pakistan U. Rasool et al. 10.1016/j.uclim.2023.101573
85. Streambed water flux characterization through a Deep-Learning-Based approach considering data worth analysis: Numerical modeling and sandbox experiments L. Ju et al. 10.1016/j.jhydrol.2022.128111
86. 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
87. 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
88. Integration of Multisource Data to Estimate Downward Longwave Radiation Based on Deep Neural Networks F. Zhu et al. 10.1109/TGRS.2021.3094321
89. Neural network based process coupling and parameter upscaling in reactive transport simulations N. Prasianakis et al. 10.1016/j.gca.2020.07.019
90. 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
91. Forecasting of water level in multiple temperate lakes using machine learning models S. Zhu et al. 10.1016/j.jhydrol.2020.124819
92. Artificial Intelligence Combined with Deep Learning in Film and Television Quality Education for the Youth D. Huang & J. Hoon-Yang 10.1142/S0219843622500190
93. 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
94. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al. 10.3390/w14142211
95. Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India P. Shekar et al. 10.1007/s10661-023-11649-0
96. Machine learning for hydrologic sciences: An introductory overview T. Xu & F. Liang 10.1002/wat2.1533
97. 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
98. Deep Convolutional LSTM for improved flash flood prediction P. Oddo et al. 10.3389/frwa.2024.1346104
99. The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China M. Liu et al. 10.3390/w12020440
100. Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling F. Rahmani et al. 10.1029/2023WR034420
101. Streamflow prediction in ungauged basins: benchmarking the efficiency of deep learning G. Ayzel et al. 10.1051/e3sconf/202016301001
102. 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
103. Towards hybrid modeling of the global hydrological cycle B. Kraft et al. 10.5194/hess-26-1579-2022
104. Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang–Mekong River Basin B. Liu et al. 10.3390/w14091429
105. Quantum cascade laser imaging (LDIR) and machine learning for the identification of environmentally exposed microplastics and polymers X. Tian et al. 10.1016/j.envres.2022.113569
106. 基于深度学习的青藏高原多源降水融合 天. 南 et al. 10.1360/SSTe-2022-0077
107. Uncertainty Forecasting Model for Mountain Flood Based on Bayesian Deep Learning S. Wang & O. Xu 10.1109/ACCESS.2024.3384066
108. 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
109. Application of machine learning approaches in supporting irrigation decision making: A review L. Umutoni & V. Samadi 10.1016/j.agwat.2024.108710
110. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
111. Developing a Physics‐Informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments L. Zhong et al. 10.1029/2022WR034118
112. Deep learning in environmental remote sensing: Achievements and challenges Q. Yuan et al. 10.1016/j.rse.2020.111716
113. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. 10.1016/j.jhydrol.2023.130038
114. Integrating geographic data and the SCS-CN method with LSTM networks for enhanced runoff forecasting in a complex mountain basin M. Merizalde et al. 10.3389/frwa.2023.1233899
115. 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
116. Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić 10.5194/hess-26-795-2022
117. Earth system data cubes unravel global multivariate dynamics M. Mahecha et al. 10.5194/esd-11-201-2020
118. 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
119. Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network D. Li et al. 10.1016/j.jhydrol.2021.126888
120. 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
121. Using a neural network – Physics-based hybrid model to predict soil reaction fronts T. Wen et al. 10.1016/j.cageo.2022.105200
122. Subsurface sedimentary structure identification using deep learning: A review C. Zhan et al. 10.1016/j.earscirev.2023.104370
123. 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
124. Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. 10.5194/hess-25-4373-2021
125. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. 10.1029/2021WR029583
126. Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links J. Pudashine et al. 10.1029/2019WR026255
127. 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
128. 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
129. Improving Precipitation Estimation Using Convolutional Neural Network B. Pan et al. 10.1029/2018WR024090
130. Developing a data-driven modeling framework for simulating a chemical accident in freshwater S. Kim et al. 10.1016/j.jclepro.2023.138842
131. KNN vs. Bluecat—Machine Learning vs. Classical Statistics E. Rozos et al. 10.3390/hydrology9060101
132. Best Management Practices for Diffuse Nutrient Pollution: Wicked Problems Across Urban and Agricultural Watersheds A. Lintern et al. 10.1021/acs.est.9b07511
133. Estimating Lake Water Volume With Regression and Machine Learning Methods C. Delaney et al. 10.3389/frwa.2022.886964
134. 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
135. 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
136. Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models D. Li et al. 10.1029/2021WR029772
137. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
138. Deep learning-based multi-source precipitation merging for the Tibetan Plateau T. Nan et al. 10.1007/s11430-022-1050-2
139. Mountain Flood Level Forecasting in Small Watersheds Based on Recurrent Neural Networks and Multi-Dimensional Data S. Wang & O. Xu 10.1109/ACCESS.2024.3412948
140. Advent of Big Data technology in environment and water management sector J. Gohil et al. 10.1007/s11356-021-14017-y
141. Lake water-level fluctuation forecasting using machine learning models: a systematic review S. Zhu et al. 10.1007/s11356-020-10917-7
142. Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks G. Ayzel et al. 10.3390/hydrology8010006
143. Advances in the Remote Sensing of Terrestrial Evaporation M. McCabe et al. 10.3390/rs11091138
144. A surrogate modeling method for distributed land surface hydrological models based on deep learning R. Sun et al. 10.1016/j.jhydrol.2023.129944
145. ANN-SFLA based parameter estimation method for an unsaturated–saturated simulation model M. Das et al. 10.1007/s40808-023-01797-0
146. Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization J. Zhang et al. 10.1029/2020WR027399
147. Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
148. Process‐Guided Deep Learning Predictions of Lake Water Temperature J. Read et al. 10.1029/2019WR024922
149. Machine learning based identification of dominant controls on runoff dynamics H. Oppel & A. Schumann 10.1002/hyp.13740
150. 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
151. Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks S. Clark et al. 10.3390/ijerph19095091
152. Machine Learning Predicts Reach‐Scale Channel Types From Coarse‐Scale Geospatial Data in a Large River Basin H. Guillon et al. 10.1029/2019WR026691
153. Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics Y. Wang et al. 10.1029/2021WR031033
154. A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk M. Rohde et al. 10.3389/feart.2021.784499
155. A Study on the Optimal Deep Learning Model for Dam Inflow Prediction B. Kim et al. 10.3390/w14172766
156. An attention mechanism based convolutional network for satellite precipitation downscaling over China Y. Jing et al. 10.1016/j.jhydrol.2022.128388
157. Machine‐Learning Reveals Equifinality in Drivers of Stream DOC Concentration at Continental Scales K. Underwood et al. 10.1029/2021WR030551
158. On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation T. Wagener et al. 10.1002/wat2.1550
159. 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