197 citations as recorded by crossref.

1. Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis Q. Tian et al.
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8. Prediction of mine water quality by the Seq2Seq model based on attention mechanism X. Wang & Y. Li
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11. Operational low-flow forecasting using LSTMs J. Deng et al.
12. RETRACTED ARTICLE: Green roofs and their effect on architectural design and urban ecology using deep learning approaches C. Wang et al.
13. Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics J. Frame et al.
14. NeuralHydrology — A Python library for Deep Learning research in hydrology F. Kratzert et al.
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19. Evaluating Grazing Management for Drought Reduction Under Different Climate Change Scenarios M. Abdulahi et al.
20. Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments P. Bhasme & U. Bhatia
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22. MSTRFormer: A Multi-Factor Daily Runoff Forecasting Model Integrating Adaptive Graph Convolution and Linear Attention W. Wang et al.
23. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi
24. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan
25. Predicting daily solar radiation using a novel hybrid long short-term memory network across four climate regions of China L. Xing et al.
26. CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland B. Kraft et al.
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28. Mind the information gap: How sampling and clustering impact the predictability of reach‐scale channel types in California (USA) H. Guillon et al.
29. Temporal changes in precipitation and correlation with large climate indicators in the Hengshao Drought Corridor, China T. Zhang et al.
30. Hydrology-informed machine learning for enhancing surface runoff elucidation P. Hua et al.
31. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change S. Wi & S. Steinschneider
32. Runoff Forecast Model Based on an EEMD-ANN and Meteorological Factors Using a Multicore Parallel Algorithm S. Liao et al.
33. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al.
34. Forecasting Multi-Step Soil Moisture with Three-Phase Hybrid Wavelet-Least Absolute Shrinkage Selection Operator-Long Short-Term Memory Network (moDWT-Lasso-LSTM) Model W. Jayasinghe et al.
35. Benchmarking data-driven rainfall-runoff modeling across 54 catchments in the Yellow River Basin: Overfitting, calibration length, dry frequency J. Jin et al.
36. A multi-temporal scale framework for comprehensive quantification and attribution of anthropogenic impacts on runoff C. Xia et al.
37. Assessment of drought conditions and prediction by machine learning algorithms using Standardized Precipitation Index and Standardized Water-Level Index (case study: Yazd province, Iran) R. Shakeri et al.
38. Incorporating long-term numerical weather forecasts to quantify dynamic vulnerability of irrigation supply system: A case study of Shihmen Reservoir in Taiwan C. Hsu & Y. Lin
39. 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
40. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al.
41. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés
42. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn
43. CAMELSH: A Large-Sample Hourly Hydrometeorological Dataset and Attributes at Watershed-Scale for CONUS V. Tran et al.
44. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River J. Xiong et al.
45. Research on Annual Runoff Prediction Model Based on Adaptive Particle Swarm Optimization–Long Short-Term Memory with Coupled Variational Mode Decomposition and Spectral Clustering Reconstruction X. Wang et al.
46. Using explainable artificial intelligence (XAI) methods to understand the nonlinear relationship between the Three Gorges Dam and downstream flood X. Wei et al.
47. Urban Rainfall Runoff Prediction Using LSTM with DTW-Enhanced Clustering Y. Tian et al.
48. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al.
49. Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić
50. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al.
51. Simulating the climate driven runoff in data-scarce mountains by machine learning and downscaling reanalysis data M. Fan et al.
52. Incorporating multi-timescale data into a single long short-term memory network to enhance reservoir-regulated streamflow simulation L. Lang et al.
53. From Climate Signals to Smart Predictions: Advanced Machine Learning and Deep Learning for Hydrological Modeling in Data-Scarce Bangladesh M. Islam et al.
54. Attribution of Runoff Variation in Reservoir Construction Area: Based on a Merged Deep Learning Model and the Budyko Framework L. Zhang et al.
55. Attributing extreme rainfall–runoff dynamics in karst watersheds: An LSTM-based mechanistic modeling and interpretation framework Z. He et al.
56. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al.
57. A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning X. Zhao et al.
58. Simulación del caudal en España utilizando redes neuronales Long Short-Term Memory J. Casado-Rodríguez et al.
59. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al.
60. Statistical learning of water budget outcomes accounting for target and feature uncertainty N. Martin & C. Yang
61. Intercomparing LSTM and RNN to a Conceptual Hydrological Model for a Low-Land River with a Focus on the Flow Duration Curve A. Ley et al.
62. Effects of mass balance, energy balance, and storage-discharge constraints on LSTM for streamflow prediction S. Pokharel et al.
63. In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance M. Gauch et al.
64. Simulated annealing coupled with a Naïve Bayes model and base flow separation for streamflow simulation in a snow dominated basin H. Tongal & M. Booij
65. Development and Applicability Assessment of an Hourly Water Level Prediction Model for Agricultural Reservoirs Using Automated Machine Learning J. Choi et al.
66. Study on the Optimization and Stability of Machine Learning Runoff Prediction Models in the Karst Area C. Mo et al.
67. Deep learning rainfall–runoff predictions of extreme events J. Frame et al.
68. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al.
69. Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach A. Alexander & D. Kumar
70. A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir P. Nguyen et al.
71. Large-scale seasonal forecasts of river discharge by coupling local and global datasets with a stacked neural network: Case for the Loire River system M. Vu et al.
72. Correction of Reservoir Runoff Forecast Based on Multi-scenario Division and Multi Models S. Wang et al.
73. 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
74. From climate signals to smart predictions: advanced machine learning and deep learning for hydrological modeling in data-scarce Bangladesh M. Islam et al.
75. Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al.
76. Runoff Estimation in the Upper Reaches of the Heihe River Using an LSTM Model with Remote Sensing Data H. Xue et al.
77. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al.
78. A precipitation-runoff swift simulation model dedicated to emergency response to flood prediction Z. Yu et al.
79. Long-term inflow forecast using meteorological data based on long short-term memory neural networks H. Zhao et al.
80. Integrative statistical analysis of compound flood dynamics under climate variability in China’s coastal zone T. Atmaja & D. Lee
81. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al.
82. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al.
83. A novel integrated multicriteria decision analysis framework for group decision-making in evapotranspiration model selection D. Awhari et al.
84. Quantum-enhanced deep learning for severe weather prediction: a 10-qubit QCNN-LSTM for bow echo forecasting H. Canot et al.
85. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang
86. When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al.
87. A Comprehensive Assessment of the Hydrological Evolution and Habitat Quality of the Xiangjiang River Basin F. Hong et al.
88. Real-time rainfall and runoff prediction by integrating BC-MODWT and automatically-tuned DNNs: Comparing different deep learning models A. Amini et al.
89. Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions S. Yoon & K. Ahn
90. rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al.
91. The optimization path of smart ice and snow sports tourism industry development under hybrid neural network model Y. Sun et al.
92. From gauged to ungauged: Large-scale deep learning rainfall-runoff modelling for reliable streamflow estimation in India's diverse basins S. Barbhuiya & V. Gupta
93. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al.
94. Event-based training data thresholds for BiLSTM versus Xinanjiang models: Insights from the applications of 19 Chinese catchments Y. Li et al.
95. Impact of spatial distribution information of rainfall in runoff simulation using deep learning method Y. Wang & H. Karimi
96. Controls on magnitude and timing of peak runoff response to rainfall across the continental US M. Li et al.
97. Fully differentiable, fully distributed rainfall-runoff modeling F. Scholz et al.
98. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al.
99. Real-time monitoring and optimization of machine learning intelligent control system in power data modeling technology Q. Wang et al.
100. Modeling and Forecasting of nanoFeCu Treated Sewage Quality Using Recurrent Neural Network (RNN) D. Cao et al.
101. Integrating deep learning and multi-objective optimization for floodwater utilization: a coordinated surface water-groundwater regulation framework for groundwater recovery L. Zhang et al.
102. Runoff Prediction Method Based on Pangu-Weather W. Yang et al.
103. Application of Artificial Intelligence in Hydrological Modeling for Streamflow Prediction in Ungauged Watersheds: A Review J. Gacu et al.
104. The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al.
105. Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods Y. Yang & T. Chui
106. Flood forecasting with machine learning models in an operational framework S. Nevo et al.
107. The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al.
108. Fast urban flood modeling informing response decisions: Model development and future perspectives T. Duan et al.
109. A Case Study: Groundwater Level Forecasting of the Gyorae Area in Actual Practice on Jeju Island Using Deep-Learning Technique D. Kim et al.
110. Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP H. Xu et al.
111. Runoff simulation driven by multi-source satellite data based on hydrological mechanism algorithm and deep learning network C. Yu et al.
112. Transition to transpiration-dominated evapotranspiration on the Loess Plateau: spatially divergent driving mechanism and threshold effect after two decades of reforestation Y. Xu et al.
113. Deep transfer learning based on transformer for flood forecasting in data-sparse basins Y. Xu et al.
114. Regarding reservoirs as switches: Deriving restored and regulated runoff with one long short-term memory model H. Ye et al.
115. Learning extreme vegetation response to climate drivers with recurrent neural networks F. Martinuzzi et al.
116. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan
117. Green Roof Hydrological Modelling With GRU and LSTM Networks H. Xie et al.
118. A Hybrid Model Combining the Cama-Flood Model and Deep Learning Methods for Streamflow Prediction M. Zhong et al.
119. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al.
120. Multi-source meteorological data assessment on daily runoff simulation in the upper reaches of the Hei River, Northwest China H. Xue et al.
121. Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model J. Aerts et al.
122. Hydrological insights from a comparative evaluation of LSTM and MC-LSTM networks in the Ebro River basin (Spain) I. González-Planet & C. Juez
123. Stream water quality optimized prediction based on human activity intensity and landscape metrics with regional heterogeneity in Taihu Basin, China Y. Wang et al.
124. Analysis of data cleansing methods for improving meteorological data quality: a case study M. Rahman & M. Khan
125. A Deep Reinforcement Learning Framework for Cascade Reservoir Operations Under Runoff Uncertainty J. Xu et al.
126. Enhancing flood prediction through physics-driven typhoon feature engineering and machine learning Z. Zhang et al.
127. Mining relevant partial periodic pattern of multi-source time series data Y. Xun et al.
128. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya T. Lees et al.
129. Causality-Structured Deep Learning for Soil Moisture Predictions L. Li et al.
130. Advancements in rainfall-runoff prediction: Exploring state-of-the-art neural computing modeling approaches D. Irwan et al.
131. A novel hybrid framework for combining process-based models with machine learning for streamflow prediction X. Jiang et al.
132. High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al.
133. Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study H. Yang & W. Li
134. The Social Interaction of Online Products in E-Commerce Live Broadcast under Generative Artificial Intelligence W. Sun et al.
135. Future projections of China runoff changes based on CMIP6 and deep learning X. Wei et al.
136. Federated learning based water streamflow forecasting via multi-sensor data fusion Y. Xia et al.
137. Enhanced streamflow forecasting using hybrid modelling integrating glacio-hydrological outputs, deep learning and wavelet transformation J. Ougahi & J. Rowan
138. Regional vs local LSTM models for short-term streamflow forecasting under operational constraints J. Saavedra-Garrido et al.
139. 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.
140. Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin C. Yang et al.
141. Earth and Planetary Surface Processes Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science C. Burberry et al.
142. Scalable big earth observation data mining algorithms: a review N. Sisodiya et al.
143. Development of a Two-Stage LSTM for Multi-Step Runoff Forecasting Using a XAJ Model and EEMD Z. Yang et al.
144. Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz
145. Neural networks in catchment hydrology: a comparative study of different algorithms in an ensemble of ungauged basins in Germany M. Weißenborn et al.
146. Streamflow forecast model using ANN D. Sonowal et al.
147. Evaluating machine learning and deep learning for upscaling instantaneous latent heat flux to daily scale E. Tunca
148. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al.
149. A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction B. Sahoo et al.
150. Enhancing Prediction Accuracy and Data Handling for Environmental Applications in Innovative Modeling of Groundwater Level Fluctuations Based on the Tree Ensembles Technique D. Chi et al.
151. Artificial intelligence with earth observations provides continuous streamflow data across varying wildfire recurrence and recovery scenarios S. Uddin et al.
152. Assessing multi-year-drought vulnerability in dense Mediterranean-climate forests using water-balance-based indicators G. Cui et al.
153. Japanese waka translation supported by internet of things and artificial intelligence technology R. Shen
154. Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning Ž. Brkić & M. Kuhta
155. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al.
156. Using Entity-Aware LSTM to Enhance Streamflow Predictions in Transboundary and Large Lake Basins Y. Park et al.
157. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al.
158. Multi-resolution adaptive channel fusion transformer encoder LSTM for accurate streamflow prediction S. Apak et al.
159. Streamflow Prediction with Time-Lag-Informed Random Forest and Its Performance Compared to SWAT in Diverse Catchments D. Moges et al.
160. Evaluation of GPM IMERG products for estimating daily precipitation, detecting extreme precipitation and monitoring meteorological droughts in Australia H. Meng et al.
161. Can Integrated Machine Learning Models Improve Rainfall Forecasting? A Case Study from the Australian Tropics R. Farooq et al.
162. A New Rainfall-Runoff Model Using Improved LSTM with Attentive Long and Short Lag-Time X. Chen et al.
163. Global to local: a novel encoder-decoder framework for urban real-time rainfall-runoff forecasting Y. Tian et al.
164. Estimating soil mineral nitrogen from data-sparse field experiments using crop model-guided deep learning approach R. Gupta et al.
165. LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China J. Li et al.
166. Evaluation of Transformer model and Self-Attention mechanism in the Yangtze River basin runoff prediction X. Wei et al.
167. Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation W. Li et al.
168. Classification of imbalanced protein sequences with deep-learning approaches; application on influenza A imbalanced virus classes R. Ahsan et al.
169. A Grid-Based Long Short-Term Memory Framework for Runoff Projection and Uncertainty in the Yellow River Source Area Under CMIP6 Climate Change H. Chu et al.
170. A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds K. Li et al.
171. Flood modelling and prediction using hydrologic similarity – driven parameter regionalization in ungauged catchments H. Lu et al.
172. Lowland transboundary river in a cold, semi-arid steppe: review of the Yesil River basin N. Ongdas et al.
173. How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models? R. Hashemi et al.
174. Spatio-temporal cross-validation to predict pluvial flood events in the Metropolitan City of Venice Z. Marco et al.
175. Prediction of groundwater drought based on hydro-meteorological insights via machine learning approaches V. Kartal et al.
176. Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting A. Amini et al.
177. Long short-term memory networks for enhancing real-time flood forecasts: a case study for an underperforming hydrologic model S. Gegenleithner et al.
178. Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al.
179. Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand P. Panyadee & P. Champrasert
180. Valuable Knowledge Mining: Deep Analysis of Heart Disease and Psychological Causes Based on Large-Scale Medical Data L. Wang et al.
181. An explainable AI approach for interpreting regionally optimized deep neural networks in hydrological prediction F. Hosseini et al.
182. Physically Consistent Runoff Simulation in Mountainous Catchments Using a Time-Varying Gated Hybrid XAJ–LSTM Model H. Shen et al.
183. Prediction of storm surge in the Pearl River Estuary based on data-driven model Q. Tian et al.
184. Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media X. Zeng & Z. Zhong
185. An efficient parallel runoff forecasting model for capturing global and local feature information Y. Hong et al.
186. Hydrological concept formation inside long short-term memory (LSTM) networks T. Lees et al.
187. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al.
188. Effect of Gradient Descent Optimizers and Dropout Technique on Deep Learning LSTM Performance in Rainfall-runoff Modeling D. Anh et al.
189. Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework L. Nguyen et al.
190. Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths M. Fathi et al.
191. Probability of rainstorm and flood disasters due to extreme precipitation in Fen River Basin, China Y. Li et al.
192. Development of objective function-based ensemble model for streamflow forecasts Y. Lin et al.
193. A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM B. Zhang et al.
194. The construction of urban cultural and creative industries using deep learning and information management Z. Zhao et al.
195. Deep Multi-Stage Reference Evapotranspiration Forecasting Model: Multivariate Empirical Mode Decomposition Integrated With the Boruta-Random Forest Algorithm W. Jayasinghe et al.
196. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al.
197. Hourly Regional Rainfall–Runoff Prediction Using Transformer with Water Conservation Constraints G. Jing et al.