104 citations as recorded by crossref.

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20. Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US S. Sarkar et al. https://doi.org/10.3390/s25020543
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28. In-Depth Examination of Machine Learning Models for the Prediction of Ground Temperature at Various Depths J. Yang & K. Dashdondov https://doi.org/10.3390/atmos14010068
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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. https://doi.org/10.1111/jfb.15603
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47. Machine Learning Models for Prediction of Shade-Affected Stream Temperatures E. Noa-Yarasca et al. https://doi.org/10.1061/JHYEFF.HEENG-6227
48. Advancing stream temperature prediction with a generalizable large-sample framework across CONUS river reaches M. Bakar Siddik et al. https://doi.org/10.1016/j.jhydrol.2025.134780
49. Soil Information on a Regional Scale: Two Machine Learning Based Approaches for Predicting Ksat H. Zeitfogel et al. https://doi.org/10.2139/ssrn.4143185
50. Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks A. Maldonado et al. https://doi.org/10.3390/math10010107
51. Near‐term lake water temperature forecasts can be used to anticipate the ecological dynamics of freshwater species R. Paíz et al. https://doi.org/10.1002/ecs2.70335
52. Deep Signals: Enhancing Bottom Temperature Predictions in Norway’s Mjøsa Lake Through VMD- and EMD-Boosted Machine Learning Models S. Oruc et al. https://doi.org/10.3390/w17182673
53. Hybrid PINN-LSTM Model for River Temperature Prediction: A Physics-Informed Deep Learning Approach M. Figueredo et al. https://doi.org/10.4236/jcc.2025.136008
54. 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 https://doi.org/10.1016/j.ecoinf.2023.102138
55. Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity H. Zeitfogel et al. https://doi.org/10.1016/j.geoderma.2023.116418
56. River thermal seasons in the Central European Plain and their changes during climate warming W. Marszelewski et al. https://doi.org/10.1016/j.jhydrol.2022.127945
57. Prediction of daily average seawater temperature using data-driven and deep learning algorithms A. Ozbek https://doi.org/10.1007/s00521-023-09010-0
58. Prediction of daily sea water temperature in Turkish seas using machine learning approaches A. Ozbek https://doi.org/10.1007/s12517-022-10893-x
59. Assessing Water Temperature and Dissolved Oxygen and Their Potential Effects on Aquatic Ecosystem Using a SARIMA Model S. Larance et al. https://doi.org/10.3390/environments12010025
60. Atmospheric controls on river temperature: Sensitivity of river temperature downstream of a dam to changes in a Mediterranean climate E. Bray et al. https://doi.org/10.1016/j.ejrh.2025.102500
61. Reconstruction of long-term water temperature indicates significant warming in Polish rivers during 1966–2020 S. Zhu et al. https://doi.org/10.1016/j.ejrh.2022.101281
62. Machine‐learning modeling of hourly potential thermal refuge area: A case study from the Sainte‐Marguerite River (Quebec, Canada) I. Hani et al. https://doi.org/10.1002/rra.4191
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65. Prediction of climate change impacts of basin-wide river temperature using rainfall-runoff-inundation (RRI) model incorporating river temperature model D. Fukumaru et al. https://doi.org/10.1007/s10201-026-00854-8
66. Protocols for Water and Environmental Modeling Using Machine Learning in California M. He et al. https://doi.org/10.3390/hydrology12030059
67. A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) L. Li et al. https://doi.org/10.5194/gmd-16-4017-2023
68. Physics-informed data-driven model for the prediction of river water temperature H. Yang & X. Xue https://doi.org/10.1016/j.engappai.2026.114386
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70. Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations Y. Song et al. https://doi.org/10.1016/j.jhydrol.2024.131573
71. A Near Real-Time Hydrological Information System for the Upper Danube Basin T. Pulka et al. https://doi.org/10.3390/hydrology8040144
72. 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. https://doi.org/10.1007/s00477-024-02803-2
73. River-lake connectivity modulates lake droughts: Comparative insights from China’s two largest river-connected lakes C. Xue et al. https://doi.org/10.1016/j.ejrh.2026.103282
74. How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology D. Bilolikar et al. https://doi.org/10.2166/nh.2025.075
75. Which strategy to improve the performances of an LSTM-based model for extreme stream temperature values? M. Saadi et al. https://doi.org/10.5194/hess-30-3623-2026
76. Long-term daily water temperatures unveil escalating water warming and intensifying heatwaves in the Odra river Basin, Central Europe J. Sun et al. https://doi.org/10.1016/j.gsf.2024.101916
77. Empirical evidence of climate change and urbanization impacts on warming stream temperatures V. Grey et al. https://doi.org/10.1016/j.watres.2023.120703
78. Long-term river water temperature reconstruction and investigation: A case study of the Dongting Lake Basin, China F. Huang et al. https://doi.org/10.1016/j.jhydrol.2022.128857
79. Insights on the climate of Bhutan from a new daily 1 km gridded data set for temperature and precipitation F. Lehner & H. Formayer https://doi.org/10.1002/joc.8125
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