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Pregled bibliografske jedinice broj: 941782

Modelling daily water temperature from air temperature for the Missouri River


(State Key Laboratory of Hydrology-Water resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nnajing, China) Zhu, Senlin; Nyarko, Emmanuel Karlo; Hadzima- Nyarko, Marijana
Modelling daily water temperature from air temperature for the Missouri River // PeerJ, 6 (2018), e4894, 19 doi:10.7717/peerj.4894 (međunarodna recenzija, članak, znanstveni)


Naslov
Modelling daily water temperature from air temperature for the Missouri River

Autori
Zhu, Senlin ; Nyarko, Emmanuel Karlo ; Hadzima- Nyarko, Marijana

Kolaboracija
State Key Laboratory of Hydrology-Water resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nnajing, China

Izvornik
PeerJ (2167-8359) 6 (2018); E4894, 19

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Water temperature ; Air temperature ; Machine learning models ; Standard regression models ; Missouri river

Sažetak
The bio-chemical and physical characteristics of a river are directly affected by water temperature, which thereby affects the overall health of aquatic ecosystems. It is a complex problem to accurately estimate water temperature. Modelling of river water temperature is usually based on a suitable mathematical model and field measurements of various atmospheric factors. In this article, the air–water temperature relationship of the Missouri River is investigated by developing three different machine learning models (Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Bootstrap Aggregated Decision Trees (BA-DT)). Standard models (linear regression, non-linear regression, and stochastic models) are also developed and compared to machine learning models. Analyzing the three standard models, the stochastic model clearly outperforms the standard linear model and nonlinear model. All the three machine learning models have comparable results and outperform the stochastic model, with GPR having slightly better results for stations No. 2 and 3, while BA-DT has slightly better results for station No. 1. The machine learning models are very effective tools which can be used for the prediction of daily river temperature.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo, Računarstvo



POVEZANOST RADA


Ustanove
Građevinski i arhitektonski fakultet Osijek,
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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