Assessing the performance of a suite of machine learning models for daily river water temperature prediction

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 (Ta), flow discharge (Q), and the day of year (DOY) as predictors. The proposed mode...

Täydet tiedot

Bibliografiset tiedot
Päätekijät: Senlin Zhu, Emmanuel Karlo Nyarko, Marijana Hadzima-Nyarko, Salim Heddam, Shiqiang Wu
Aineistotyyppi: Artikkeli
Kieli:English
Julkaistu: PeerJ Inc. 2019-06-01
Sarja:PeerJ
Aiheet:
River water temperature
Artificial neural network
Flow discharge
Decision tree
Air temperature
Gaussian process regression
Linkit:https://peerj.com/articles/7065.pdf

Internet

https://peerj.com/articles/7065.pdf

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