A New Approach for Assessing Heat Balance State along a Water Transfer Channel during Winter Periods

Cheng, Tiejie; Wang, Jun; Sui, Jueyi; Zhao, Haidong; Hao, Zhixin; Huang, Minghai; Li, Zhicong

doi:10.3390/w14203269

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…The statistical error indices demonstrate that the suggested models exhibit satisfactory accuracy in estimating the flow discharge in the CCNPF. Cheng et al (2022) conducted measurements of water temperature and environmental variables at many locations in the primary channel of the middle part of China's South-to-North Water Transfer Project. The heat balance status of this water transfer route has been analysed using the provided temperature data.…”

Section: Introductionmentioning

confidence: 99%

Prediction of shear stress distribution in compound channel with smooth converging floodplains

Kaushik,

Kumar

2024

Journal of Hydrology and Hydromechanics

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Climate change can have a profound impact on river flooding, leading to increased frequency and severity of floods. To mitigate these effects, it is crucial to focus on enhancing early warning systems and bolstering infrastructure resilience through improved forecasting. This proactive approach enables communities to better plan for and respond to flood events, thereby minimizing the adverse consequences of climate change on river floods. During river flooding, the channels often take on a compound nature, with varying geometries along the flow length. This complexity arises from construction and agricultural activities along the floodplains, resulting in converging, diverging, or skewed compound channels. Modelling the flow in these channels requires consideration of additional momentum transfer factors. In this study, machine learning techniques, including Gene Expression Programming (GEP), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), were employed. The focus was on a compound channel with converging floodplains, predicting the shear force carried by the floodplains in terms of non-dimensional flow and hydraulic parameters. The findings indicate that the proposed ANN model outperformed GEP, SVM, and other established approaches in accurately predicting floodplain shear force. This research underscores the efficacy of utilizing machine learning techniques in the examination of river hydraulics.

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Section: Introductionmentioning

confidence: 99%

Prediction of shear stress distribution in compound channel with smooth converging floodplains

Kaushik,

Kumar

2024

Journal of Hydrology and Hydromechanics

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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

Cheng,

Wang,

Sui

et al. 2024

Journal of Hydrology and Hydromechanics

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In winter, the water transfer channel of the Middle Route of South-to-North Water Transfer Project (MR-StNWTP) in China always encounters ice problems. The preciously simulation and prediction of water temperature is essential for analyzing the ice condition, which is important for the safety control of the water transfer channel in winter. Due to the difference of specific heat between water and air, when the air temperature rises and falls dramatically, the range of change of water temperature is relatively small and has a lag, which often affects the accuracy of simulation and prediction of water temperature based on air temperature. In the present study, a new approach for simulating and predicting water temperature in water transfer channels in winter has been proposed. By coupling the neural network theory to equations describing water temperature, a model has been developed for predicting water temperature. The temperature data of prototype observations in winter are preprocessed through the wavelet decomposition and noise reduction. Then, the wavelet soft threshold denoising method is used to eliminate the fluctuation of certain temperature data of prototype observations, and the corresponding water temperature is calculated afterward. Compared to calculation results using both general neural network and multiple regression approaches, the calculation results using the proposed model agree well with those of prototype measurements and can effectively improve the accuracy of prediction of water temperature.

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A New Approach for Assessing Heat Balance State along a Water Transfer Channel during Winter Periods

Cited by 2 publications

References 33 publications

Prediction of shear stress distribution in compound channel with smooth converging floodplains

Prediction of shear stress distribution in compound channel with smooth converging floodplains

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

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