67 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
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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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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
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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. 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. Assessing the impact of climate change and reservoir operation on the thermal and ice regime of mountain rivers using the XGBoost model and wavelet analysis M. Fukś et al. 10.1007/s00477-024-02803-2
18. 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
19. An optimized NARX-based model for predicting thermal dynamics and heatwaves in rivers S. Zhu et al. 10.1016/j.scitotenv.2024.171954
20. 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
21. 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
22. 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
23. Long-term daily water temperatures unveil escalating water warming and intensifying heatwaves in the Odra river Basin, Central Europe J. Sun et al. 10.1016/j.gsf.2024.101916
24. Empirical evidence of climate change and urbanization impacts on warming stream temperatures V. Grey et al. 10.1016/j.watres.2023.120703
25. 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
26. 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
27. 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
28. 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
29. 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
30. In-Depth Examination of Machine Learning Models for the Prediction of Ground Temperature at Various Depths J. Yang & K. Dashdondov 10.3390/atmos14010068
31. Hierarchical neural network-based hydrological perception model for underwater glider L. Lei et al. 10.1016/j.oceaneng.2022.112101
32. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
33. Österreichweite Regionalisierung bodenhydraulischer Eigenschaften H. Zeitfogel et al. 10.1007/s00506-022-00842-z
34. 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
35. 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
36. A random forest approach to improve estimates of tributary nutrient loading P. Isles 10.1016/j.watres.2023.120876
37. Effects of multicollinearity and data granularity on regression models of stream temperature H. Dertli et al. 10.1016/j.jhydrol.2024.131572
38. Forecasting personal heat strain under extremely hot environments: Utilizing feature importance in machine learning S. Seo et al. 10.1016/j.engappai.2024.108507
39. River Water Temperature Prediction Using a Hybrid Model Based on Variational Mode Decomposition (VMD) and Outlier Robust Extreme Learning Machine E. Mirzania et al. 10.1007/s40710-024-00716-4
40. Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois A. Bhusal et al. 10.3390/hydrology9070117
41. Optimizing building energy performance predictions: A comparative study of artificial intelligence models O. Alawi et al. 10.1016/j.jobe.2024.109247
42. 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
43. 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
44. Regime shifts in the thermal dynamics of offshore China due to accelerated global warming Y. Wang et al. 10.1016/j.scitotenv.2024.174882
45. Predicting Daily River Chlorophyll Concentrations at a Continental Scale P. Savoy & J. Harvey 10.1029/2022WR034215
46. 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
47. Prediction of water temperature of Sebou estuary (Morocco) using ANN and LR A. Touazit et al. 10.1080/23249676.2024.2303150
48. 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
49. 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
50. Machine Learning Models for Prediction of Shade-Affected Stream Temperatures E. Noa-Yarasca et al. 10.1061/JHYEFF.HEENG-6227
51. Soil Information on a Regional Scale: Two Machine Learning Based Approaches for Predicting Ksat H. Zeitfogel et al. 10.2139/ssrn.4143185
52. Dynamic rates of Freshwater Drum near the northern extent of their range: evidence of environment-recruitment relationships M. Stuart et al. 10.1007/s10641-024-01589-0
53. Historical and projected response of Southeast Asian lakes surface water temperature to warming climate S. Virdis et al. 10.1016/j.envres.2024.118412
54. Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks A. Maldonado et al. 10.3390/math10010107
55. 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
56. 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
57. River thermal seasons in the Central European Plain and their changes during climate warming W. Marszelewski et al. 10.1016/j.jhydrol.2022.127945
58. Prediction of daily average seawater temperature using data-driven and deep learning algorithms A. Ozbek 10.1007/s00521-023-09010-0
59. Prediction of daily sea water temperature in Turkish seas using machine learning approaches A. Ozbek 10.1007/s12517-022-10893-x
60. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
61. 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
62. 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
63. Vorhersage der Fließgewässertemperaturen in österreichischen Einzugsgebieten mittels Machine Learning-Verfahren M. Feigl et al. 10.1007/s00506-021-00771-3
64. Assessment of Impacts of Climate Change on Indian Riverine Thermal Regimes Using Hybrid Deep Learning Methods S. Rehana & M. Rajesh 10.1029/2021WR031347
65. 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
66. 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
67. 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