Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
<p>The prediction of river water temperature is of key importance in the field of environmental science. Water temperature datasets for low-order rivers are often in short supply, leaving environmental modelers with the challenge of extracting as much information as possible from existing data...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-07-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/16/4083/2023/gmd-16-4083-2023.pdf |
| Summary: | <p>The prediction of river water temperature is of key importance in the field
of environmental science. Water temperature datasets for low-order rivers
are often in short supply, leaving environmental modelers with the challenge
of extracting as much information as possible from existing datasets.
Therefore, identifying a suitable modeling solution for the prediction of
river water temperature with a large scarcity of forcing datasets is of
great importance. In this study, five models, forced with the meteorological
datasets obtained from the fifth-generation atmospheric reanalysis,
ERA5-Land, are used to predict the water temperature of 83 rivers (with
98 % missing data): three machine learning algorithms (random forest,
artificial neural network and support vector regression), the hybrid
Air2stream model with all available parameterizations and a multiple
regression. The machine learning hyperparameters were optimized with a
tree-structured Parzen estimator, and an oversampling–undersampling technique was
used to generate synthetic training datasets. In general terms, the results
of the study demonstrate the vital importance of hyperparameter optimization
and suggest that, from a practical modeling perspective, when the number of
predictor variables and observed river water temperature values are limited,
the application of all the models considered in this study is crucial.
Basically, all the models tested proved to be the best for at least one
station. The root mean square error (RMSE) and the Nash–Sutcliffe efficiency
(NSE) values obtained for the ensemble of all model results were
<span class="inline-formula">2.75±1.00</span> and <span class="inline-formula">0.56±0.48</span> <span class="inline-formula"><sup>∘</sup></span>C, respectively. The model that performed the best overall was random
forest (annual mean – RMSE: <span class="inline-formula">3.18±1.06</span> <span class="inline-formula"><sup>∘</sup></span>C; NSE: <span class="inline-formula">0.52±0.23</span>). With the application of the oversampling–undersampling technique, the
RMSE values obtained with the random forest model were reduced from 0.00 %
to 21.89 % (<span class="inline-formula"><i>μ</i>=8.57</span> %; <span class="inline-formula"><i>σ</i>=8.21</span> %) and the NSE values
increased from 1.1 % to 217.0 % (<span class="inline-formula"><i>μ</i>=40</span> %; <span class="inline-formula"><i>σ</i>=63</span> %).
These results suggest that the solution proposed has the potential to
significantly improve the modeling of water temperature in rivers with
machine learning methods, as well as providing increased scope for its
application to larger training datasets and the prediction of other types of
dependent variables. The results also revealed the existence of a
logarithmic correlation among the RMSE between the observed and predicted
river water temperature and the watershed time of concentration. The RMSE
increases by an average of 0.1 <span class="inline-formula"><sup>∘</sup></span>C with a 1 h increase
in the watershed time of concentration (watershed area: <span class="inline-formula"><i>μ</i>=106</span> km<span class="inline-formula"><sup>2</sup></span>; <span class="inline-formula"><i>σ</i>=153</span>).</p> |
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| ISSN: | 1991-959X 1991-9603 |