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Assessing the performance of a suite of machine learning models for daily river water temperature prediction
In this study, different versions of feedforward neural network (FFNN), Gaussian process regression (GPR), and decision tree (DT) models were developed to estimate daily river water temperature using air temperature (T(a)), flow discharge (Q), and the day of year (DOY) as predictors. The proposed mo...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
PeerJ Inc.
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6555394/ https://www.ncbi.nlm.nih.gov/pubmed/31198649 http://dx.doi.org/10.7717/peerj.7065 |