60 citations as recorded by crossref.

1. Machine‐learning modeling of hourly potential thermal refuge area: A case study from the Sainte‐Marguerite River (Quebec, Canada) I. Hani et al. 10.1002/rra.4191
2. Prediction of phytoplankton biomass and identification of key influencing factors using interpretable machine learning models Y. Xu et al. 10.1016/j.ecolind.2023.111320
3. Warming of the Willamette River, 1850–present: the effects of climate change and river system alterations S. Talke et al. 10.5194/hess-27-2807-2023
4. Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023) M. Niazkar et al. 10.1016/j.envsoft.2024.105971
5. Variable wildfire impacts on the seasonal water temperatures of western US streams: A retrospective study M. Beyene et al. 10.1371/journal.pone.0268452
6. Water outlet temperature prediction method of nuclear power plant based on echo state network with variable memory length D. Yu et al. 10.3233/JCM-226735
7. Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling F. Rahmani et al. 10.1029/2023WR034420
8. Drought impacts on river water temperature: A process‐based understanding from temperate climates J. White et al. 10.1002/hyp.14958
9. Learning from mistakes—Assessing the performance and uncertainty in process‐based models M. Feigl et al. 10.1002/hyp.14515
10. A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) L. Li et al. 10.5194/gmd-16-4017-2023
11. Rising summer river water temperature across Canada: spatial patterns and hydroclimatic controls R. Shrestha et al. 10.1088/1748-9326/ad365f
12. DEVELOPMENT OF RIVER WATER TEMPERATURE PREDICTION MODEL AT THE LOWEST REACH BY RECURRENT NEURAL NETWORK N. HARAGUCHI et al. 10.2208/jscejhe.77.2_I_1219
13. Predicting stream water temperature with artificial neural networks based on open‐access data K. Drainas et al. 10.1002/hyp.14991
14. Application of a modern multi-level ensemble approach for the estimation of critical shear stress in cohesive sediment mixture U. Singh et al. 10.1016/j.jhydrol.2022.127549
15. Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations Y. Song et al. 10.1016/j.jhydrol.2024.131573
16. A Near Real-Time Hydrological Information System for the Upper Danube Basin T. Pulka et al. 10.3390/hydrology8040144
17. Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning H. Weierbach et al. 10.3390/w14071032
18. An optimized NARX-based model for predicting thermal dynamics and heatwaves in rivers S. Zhu et al. 10.1016/j.scitotenv.2024.171954
19. Regional thermal analysis approach: A management tool for predicting water temperature metrics relevant for thermal fish habitat O. Abidi et al. 10.1016/j.ecoinf.2022.101692
20. Possibilities of River Water Temperature Reconstruction Using Statistical Models in the Context of Long-Term Thermal Regime Changes Assessment M. Sojka & M. Ptak 10.3390/app12157503
21. Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression M. Almeida & P. Coelho 10.5194/gmd-16-4083-2023
22. Empirical evidence of climate change and urbanization impacts on warming stream temperatures V. Grey et al. 10.1016/j.watres.2023.120703
23. Climate warming effects in stratified reservoirs: Thorough assessment for opportunities and limits of machine learning techniques versus process-based models in thermal structure projections C. Mi et al. 10.1016/j.jclepro.2024.142347
24. Long-term river water temperature reconstruction and investigation: A case study of the Dongting Lake Basin, China F. Huang et al. 10.1016/j.jhydrol.2022.128857
25. Insights on the climate of Bhutan from a new daily 1 km gridded data set for temperature and precipitation F. Lehner & H. Formayer 10.1002/joc.8125
26. Development of a Multilayer Deep Neural Network Model for Predicting Hourly River Water Temperature From Meteorological Data R. Abdi et al. 10.3389/fenvs.2021.738322
27. Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer M. Karbasi et al. 10.1016/j.compag.2022.107121
28. In-Depth Examination of Machine Learning Models for the Prediction of Ground Temperature at Various Depths J. Yang & K. Dashdondov 10.3390/atmos14010068
29. Hierarchical neural network-based hydrological perception model for underwater glider L. Lei et al. 10.1016/j.oceaneng.2022.112101
30. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
31. Österreichweite Regionalisierung bodenhydraulischer Eigenschaften H. Zeitfogel et al. 10.1007/s00506-022-00842-z
32. An advanced approach for the precise prediction of water quality using a discrete hidden markov model D. Li et al. 10.1016/j.jhydrol.2022.127659
33. Designing a decomposition-based multi-phase pre-processing strategy coupled with EDBi-LSTM deep learning approach for sediment load forecasting M. Jamei et al. 10.1016/j.ecolind.2023.110478
34. A random forest approach to improve estimates of tributary nutrient loading P. Isles 10.1016/j.watres.2023.120876
35. Forecasting personal heat strain under extremely hot environments: Utilizing feature importance in machine learning S. Seo et al. 10.1016/j.engappai.2024.108507
36. Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois A. Bhusal et al. 10.3390/hydrology9070117
37. Optimizing building energy performance predictions: A comparative study of artificial intelligence models O. Alawi et al. 10.1016/j.jobe.2024.109247
38. A transfer learning-based long short-term memory model for the prediction of river water temperature J. Chen & X. Xue 10.1016/j.engappai.2024.108605
39. Data driven insights for parabolic trough solar collectors: Artificial intelligence-based energy and exergy performance analysis H. Tao et al. 10.1016/j.jclepro.2024.141069
40. Predicting Daily River Chlorophyll Concentrations at a Continental Scale P. Savoy & J. Harvey 10.1029/2022WR034215
41. A comparative study of daily streamflow forecasting using firefly, artificial bee colony, and genetic algorithm-based artificial neural network H. Kilinc et al. 10.1007/s11600-024-01362-y
42. Prediction of water temperature of Sebou estuary (Morocco) using ANN and LR A. Touazit et al. 10.1080/23249676.2024.2303150
43. Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar A. Rinaldo et al. 10.1111/jfb.15603
44. Air quality monitoring based on chemical and meteorological drivers: Application of a novel data filtering-based hybridized deep learning model M. Jamei et al. 10.1016/j.jclepro.2022.134011
45. Soil Information on a Regional Scale: Two Machine Learning Based Approaches for Predicting Ksat H. Zeitfogel et al. 10.2139/ssrn.4143185
46. Historical and projected response of Southeast Asian lakes surface water temperature to warming climate S. Virdis et al. 10.1016/j.envres.2024.118412
47. Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks A. Maldonado et al. 10.3390/math10010107
48. An integrated approach based on the correction of imbalanced small datasets and the application of machine learning algorithms to predict total phosphorus concentration in rivers M. Almeida & P. Coelho 10.1016/j.ecoinf.2023.102138
49. Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity H. Zeitfogel et al. 10.1016/j.geoderma.2023.116418
50. River thermal seasons in the Central European Plain and their changes during climate warming W. Marszelewski et al. 10.1016/j.jhydrol.2022.127945
51. Prediction of daily average seawater temperature using data-driven and deep learning algorithms A. Ozbek 10.1007/s00521-023-09010-0
52. Prediction of daily sea water temperature in Turkish seas using machine learning approaches A. Ozbek 10.1007/s12517-022-10893-x
53. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
54. Reconstruction of long-term water temperature indicates significant warming in Polish rivers during 1966–2020 S. Zhu et al. 10.1016/j.ejrh.2022.101281
55. Application of an explainable glass-box machine learning approach for prognostic analysis of a biogas-powered small agriculture engine M. Jamei et al. 10.1016/j.energy.2023.129862
56. Vorhersage der Fließgewässertemperaturen in österreichischen Einzugsgebieten mittels Machine Learning-Verfahren M. Feigl et al. 10.1007/s00506-021-00771-3
57. Assessment of Impacts of Climate Change on Indian Riverine Thermal Regimes Using Hybrid Deep Learning Methods S. Rehana & M. Rajesh 10.1029/2021WR031347
58. Prediction of land surface temperature of major coastal cities of India using bidirectional LSTM neural networks R. Maddu et al. 10.2166/wcc.2021.460
59. Impact of climate change on river water temperature and dissolved oxygen: Indian riverine thermal regimes M. Rajesh & S. Rehana 10.1038/s41598-022-12996-7
60. Monthly stream temperatures along the Danube River: Statistical analysis and predictive modelling with incremental climate change scenarios P. Pekárová et al. 10.2478/johh-2023-0028