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Simulation and prediction of water temperature in a water transfer channel during winter periods using a new approach based on the wavelet noise reduction-deep learning method Cover

Simulation and prediction of water temperature in a water transfer channel during winter periods using a new approach based on the wavelet noise reduction-deep learning method

Journal of Hydrology and Hydromechanics
Volume 72 (2024): Issue 1 (March 2024)
By: Tiejie Cheng,  Jun Wang,  Jueyi Sui,  Feihu Song,  Hui Fu,  Tao Wang and  Xinlei Guo  
Open Access
|Feb 2024

Authors

Tiejie Cheng

School of Civil Engineering, Hefei University of Technology, Anhui, China

Jun Wang

School of Civil Engineering, Hefei University of Technology, Anhui, China

Jueyi Sui

jueyi.sui@unbc.ca

School of Engineering, University of Northern British Columbia, Canada

Feihu Song

School of Civil Engineering, Hefei University of Technology, Anhui, China

Hui Fu

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China

Tao Wang

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China

Xinlei Guo

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
DOI: https://doi.org/10.2478/johh-2023-0036 | Journal eISSN: 1338-4333 | Journal ISSN: 0042-790X
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Language: English
Page range: 49 - 63
Submitted on: Feb 20, 2023
Accepted on: Jun 15, 2023
Published on: Feb 8, 2024
Published by: Slovak Academy of Sciences, Institute of Hydrology; Institute of Hydrodynamics, Czech Academy of Sciences, Prague
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Neural network,
Water transfer project,
Water temperature,
Wavelet noise reduction
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2024 Tiejie Cheng, Jun Wang, Jueyi Sui, Feihu Song, Hui Fu, Tao Wang, Xinlei Guo, published by Slovak Academy of Sciences, Institute of Hydrology; Institute of Hydrodynamics, Czech Academy of Sciences, Prague
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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