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...

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Detalles Bibliográficos
Autores principales: Zhu, Senlin, Nyarko, Emmanuel Karlo, Hadzima-Nyarko, Marijana, Heddam, Salim, Wu, Shiqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Computational Science
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

Internet

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

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