55 citations as recorded by crossref.

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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
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11. Rising summer river water temperature across Canada: spatial patterns and hydroclimatic controls R. Shrestha et al. 10.1088/1748-9326/ad365f
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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. A Near Real-Time Hydrological Information System for the Upper Danube Basin T. Pulka et al. 10.3390/hydrology8040144
16. 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
17. An optimized NARX-based model for predicting thermal dynamics and heatwaves in rivers S. Zhu et al. 10.1016/j.scitotenv.2024.171954
18. 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
19. 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
20. 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
21. Empirical evidence of climate change and urbanization impacts on warming stream temperatures V. Grey et al. 10.1016/j.watres.2023.120703
22. 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
23. 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
24. 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
25. 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
26. In-Depth Examination of Machine Learning Models for the Prediction of Ground Temperature at Various Depths J. Yang & K. Dashdondov 10.3390/atmos14010068
27. Hierarchical neural network-based hydrological perception model for underwater glider L. Lei et al. 10.1016/j.oceaneng.2022.112101
28. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
29. Österreichweite Regionalisierung bodenhydraulischer Eigenschaften H. Zeitfogel et al. 10.1007/s00506-022-00842-z
30. 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
31. 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
32. A random forest approach to improve estimates of tributary nutrient loading P. Isles 10.1016/j.watres.2023.120876
33. Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois A. Bhusal et al. 10.3390/hydrology9070117
34. Optimizing building energy performance predictions: A comparative study of artificial intelligence models O. Alawi et al. 10.1016/j.jobe.2024.109247
35. 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
36. Predicting Daily River Chlorophyll Concentrations at a Continental Scale P. Savoy & J. Harvey 10.1029/2022WR034215
37. Prediction of water temperature of Sebou estuary (Morocco) using ANN and LR A. Touazit et al. 10.1080/23249676.2024.2303150
38. 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
39. 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
40. Soil Information on a Regional Scale: Two Machine Learning Based Approaches for Predicting Ksat H. Zeitfogel et al. 10.2139/ssrn.4143185
41. Historical and projected response of Southeast Asian lakes surface water temperature to warming climate S. Virdis et al. 10.1016/j.envres.2024.118412
42. Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks A. Maldonado et al. 10.3390/math10010107
43. 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
44. 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
45. River thermal seasons in the Central European Plain and their changes during climate warming W. Marszelewski et al. 10.1016/j.jhydrol.2022.127945
46. Prediction of daily average seawater temperature using data-driven and deep learning algorithms A. Ozbek 10.1007/s00521-023-09010-0
47. Prediction of daily sea water temperature in Turkish seas using machine learning approaches A. Ozbek 10.1007/s12517-022-10893-x
48. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
49. 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
50. 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
51. Vorhersage der Fließgewässertemperaturen in österreichischen Einzugsgebieten mittels Machine Learning-Verfahren M. Feigl et al. 10.1007/s00506-021-00771-3
52. Assessment of Impacts of Climate Change on Indian Riverine Thermal Regimes Using Hybrid Deep Learning Methods S. Rehana & M. Rajesh 10.1029/2021WR031347
53. 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
54. 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
55. 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