76 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. Identifying the combined impact of human activities and natural factors on China's avian species richness using interpretable machine learning methods Q. Liu et al. 10.1016/j.jenvman.2025.124479
4. 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
5. 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
6. Variable wildfire impacts on the seasonal water temperatures of western US streams: A retrospective study M. Beyene et al. 10.1371/journal.pone.0268452
7. 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
8. Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling F. Rahmani et al. 10.1029/2023WR034420
9. Drought impacts on river water temperature: A process‐based understanding from temperate climates J. White et al. 10.1002/hyp.14958
10. Learning from mistakes—Assessing the performance and uncertainty in process‐based models M. Feigl et al. 10.1002/hyp.14515
11. Protocols for Water and Environmental Modeling Using Machine Learning in California M. He et al. 10.3390/hydrology12030059
12. 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
13. Rising summer river water temperature across Canada: spatial patterns and hydroclimatic controls R. Shrestha et al. 10.1088/1748-9326/ad365f
14. 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
15. Predicting stream water temperature with artificial neural networks based on open‐access data K. Drainas et al. 10.1002/hyp.14991
16. 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
17. 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
18. A Near Real-Time Hydrological Information System for the Upper Danube Basin T. Pulka et al. 10.3390/hydrology8040144
19. 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
20. 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
21. An optimized NARX-based model for predicting thermal dynamics and heatwaves in rivers S. Zhu et al. 10.1016/j.scitotenv.2024.171954
22. Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US S. Sarkar et al. 10.3390/s25020543
23. 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
24. 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
25. 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
26. 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
27. Empirical evidence of climate change and urbanization impacts on warming stream temperatures V. Grey et al. 10.1016/j.watres.2023.120703
28. 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
29. 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
30. 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
31. 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
32. 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
33. In-Depth Examination of Machine Learning Models for the Prediction of Ground Temperature at Various Depths J. Yang & K. Dashdondov 10.3390/atmos14010068
34. Hierarchical neural network-based hydrological perception model for underwater glider L. Lei et al. 10.1016/j.oceaneng.2022.112101
35. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
36. Österreichweite Regionalisierung bodenhydraulischer Eigenschaften H. Zeitfogel et al. 10.1007/s00506-022-00842-z
37. 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
38. 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
39. A random forest approach to improve estimates of tributary nutrient loading P. Isles 10.1016/j.watres.2023.120876
40. Effects of multicollinearity and data granularity on regression models of stream temperature H. Dertli et al. 10.1016/j.jhydrol.2024.131572
41. Forecasting personal heat strain under extremely hot environments: Utilizing feature importance in machine learning S. Seo et al. 10.1016/j.engappai.2024.108507
42. 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
43. Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois A. Bhusal et al. 10.3390/hydrology9070117
44. Optimizing building energy performance predictions: A comparative study of artificial intelligence models O. Alawi et al. 10.1016/j.jobe.2024.109247
45. 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
46. 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
47. Regime shifts in the thermal dynamics of offshore China due to accelerated global warming Y. Wang et al. 10.1016/j.scitotenv.2024.174882
48. Predicting Daily River Chlorophyll Concentrations at a Continental Scale P. Savoy & J. Harvey 10.1029/2022WR034215
49. 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
50. Prediction of water temperature of Sebou estuary (Morocco) using ANN and LR A. Touazit et al. 10.1080/23249676.2024.2303150
51. 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
52. Rivers increasingly warmer: Prediction of changes in the thermal regime of rivers in Poland M. Ptak et al. 10.1007/s11442-025-2316-5
53. Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources J. Willard et al. 10.1017/eds.2024.14
54. 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
55. Machine Learning Models for Prediction of Shade-Affected Stream Temperatures E. Noa-Yarasca et al. 10.1061/JHYEFF.HEENG-6227
56. Soil Information on a Regional Scale: Two Machine Learning Based Approaches for Predicting Ksat H. Zeitfogel et al. 10.2139/ssrn.4143185
57. 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
58. Historical and projected response of Southeast Asian lakes surface water temperature to warming climate S. Virdis et al. 10.1016/j.envres.2024.118412
59. Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks A. Maldonado et al. 10.3390/math10010107
60. 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
61. 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
62. Interconnections, trend analysis and forecasting of water-air temperature with water level dynamics in Blue Moon Lake Valley: A statistical and machine learning approach S. Shah et al. 10.1016/j.jenvman.2025.124829
63. River thermal seasons in the Central European Plain and their changes during climate warming W. Marszelewski et al. 10.1016/j.jhydrol.2022.127945
64. Prediction of daily average seawater temperature using data-driven and deep learning algorithms A. Ozbek 10.1007/s00521-023-09010-0
65. Prediction of daily sea water temperature in Turkish seas using machine learning approaches A. Ozbek 10.1007/s12517-022-10893-x
66. Predicting Post-Wildfire Stream Temperature and Turbidity: A Machine Learning Approach in Western U.S. Watersheds J. Chen & H. Chang 10.3390/w17030359
67. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
68. Assessing Water Temperature and Dissolved Oxygen and Their Potential Effects on Aquatic Ecosystem Using a SARIMA Model S. Larance et al. 10.3390/environments12010025
69. Coupled convolutional neural network with long short-term memory network for predicting lake water temperature H. Yang et al. 10.1016/j.jhydrol.2025.132878
70. 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
71. 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
72. Vorhersage der Fließgewässertemperaturen in österreichischen Einzugsgebieten mittels Machine Learning-Verfahren M. Feigl et al. 10.1007/s00506-021-00771-3
73. Assessment of Impacts of Climate Change on Indian Riverine Thermal Regimes Using Hybrid Deep Learning Methods S. Rehana & M. Rajesh 10.1029/2021WR031347
74. 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
75. 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
76. 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