Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4083-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4083-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
20 Jul 2023
Model evaluation paper |
| 20 Jul 2023
| 20 Jul 2023
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
MARE – Marine and Environmental Sciences Centre, ARNET – Aquatic
Research Network Associate Laboratory, NOVA School of Science and
Technology, NOVA University Lisbon, Caparica, Portugal
Pedro S. Coelho
MARE – Marine and Environmental Sciences Centre, ARNET – Aquatic
Research Network Associate Laboratory, NOVA School of Science and
Technology, NOVA University Lisbon, Caparica, Portugal
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Cited
20 citations as recorded by crossref.
- Modeling and mapping sea surface gage height using satellite remote sensing data N. Suwal & Z. Deng
- Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India J. Rajput et al.
- Methods for predicting water temperature in data-scarce areas under different climate regions of China J. Zhang et al.
- Optimizing models for the prediction of one step ahead extreme flows to wastewater treatment plants using different synthetic sampling methods I. Musaazi et al.
- From climate change to dam construction: A multi-stressor analysis of global river water temperature change J. Gao et al.
- Predictive Modeling of River Water Temperatures in Catu River: A Neural Network‐Based Approach C. Silva et al.
- Rising summer river water temperature across Canada: spatial patterns and hydroclimatic controls R. Shrestha et al.
- Prediction of daily river water temperatures using an optimized model based on NARX networks J. Sun et al.
- Long-term daily water temperatures unveil escalating water warming and intensifying heatwaves in the Odra river Basin, Central Europe J. Sun et al.
- Unlocking Canada’s rivers: a novel classification of thermal regimes using advanced clustering techniques S. Pokorny et al.
- Projected river water temperatures in Poland under climate change scenarios W. Dong et al.
- RetroSight and ForeSight ensemble model (ReForM) for improved time series prediction: A case study on river temperature prediction F. Bagheri et al.
- Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model H. Seo & Y. Kim
- Hybrid PINN-LSTM Model for River Temperature Prediction: A Physics-Informed Deep Learning Approach M. Figueredo et al.
- Redefining inland water temperature forecasting with advanced neural architectures F. Granata et al.
- A novel method for frequency analysis of high water temperatures using temperature duration curves in a partially regulated watershed M. Khorsandi & S. Déry
- An optimized NARX-based model for predicting thermal dynamics and heatwaves in rivers S. Zhu et al.
- Streamflow forecasting using machine learning and remote sensing data in the Himalayan region N. Suwal et al.
- HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network T. Nguyen et al.
- River water temperature prediction using hybrid machine learning coupled signal decomposition: EWT versus MODWT S. Heddam et al.
20 citations as recorded by crossref.
- Modeling and mapping sea surface gage height using satellite remote sensing data N. Suwal & Z. Deng
- Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India J. Rajput et al.
- Methods for predicting water temperature in data-scarce areas under different climate regions of China J. Zhang et al.
- Optimizing models for the prediction of one step ahead extreme flows to wastewater treatment plants using different synthetic sampling methods I. Musaazi et al.
- From climate change to dam construction: A multi-stressor analysis of global river water temperature change J. Gao et al.
- Predictive Modeling of River Water Temperatures in Catu River: A Neural Network‐Based Approach C. Silva et al.
- Rising summer river water temperature across Canada: spatial patterns and hydroclimatic controls R. Shrestha et al.
- Prediction of daily river water temperatures using an optimized model based on NARX networks J. Sun et al.
- Long-term daily water temperatures unveil escalating water warming and intensifying heatwaves in the Odra river Basin, Central Europe J. Sun et al.
- Unlocking Canada’s rivers: a novel classification of thermal regimes using advanced clustering techniques S. Pokorny et al.
- Projected river water temperatures in Poland under climate change scenarios W. Dong et al.
- RetroSight and ForeSight ensemble model (ReForM) for improved time series prediction: A case study on river temperature prediction F. Bagheri et al.
- Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model H. Seo & Y. Kim
- Hybrid PINN-LSTM Model for River Temperature Prediction: A Physics-Informed Deep Learning Approach M. Figueredo et al.
- Redefining inland water temperature forecasting with advanced neural architectures F. Granata et al.
- A novel method for frequency analysis of high water temperatures using temperature duration curves in a partially regulated watershed M. Khorsandi & S. Déry
- An optimized NARX-based model for predicting thermal dynamics and heatwaves in rivers S. Zhu et al.
- Streamflow forecasting using machine learning and remote sensing data in the Himalayan region N. Suwal et al.
- HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network T. Nguyen et al.
- River water temperature prediction using hybrid machine learning coupled signal decomposition: EWT versus MODWT S. Heddam et al.
Saved (final revised paper)
Latest update: 06 May 2026
Short summary
Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different...
