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Daily suspended sediment forecast by an integrated dynamic neural network
Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.ORCID iD: 0000-0003-0820-617X
Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden; Vattenfall AB, R&D Hydraulic Laboratory, Älvkarleby, Sweden .
2022 (English)In: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 604, article id 127258Article in journal (Refereed) Published
Abstract [en]

Suspended sediment is of importance in river and dam engineering. While, due to its high nonlinearity and stochasticity, sediment prediction by conventional methods is a challenging task. Consequently, this paper establishes a new hybrid model for an improved forecast of suspended sediment concentration (SSC). It is a nonlinear autoregressive network with exogenous inputs (NARX) integrated with a data pre-processing framework (denoted as INARX). In this model, wavelet transformation (WT) is used for time series decomposition and multigene genetic programing (MGGP) for details scaling. The two incorporated modules improve time and frequency domain analysis, allowing the network to unveil the embedded characteristics and capture its non-stationarity. At a hydrological station on the upper reaches of the Yangtze River, the records of daily water stage, flow discharge and suspended sediment are collected and refer to a nine-year period during 2004-2012. The data are used to evaluate the models. Several wavelets are explored, showing that the Coif3 leads to the most accurate prediction. Compared to the sediment rating curve (SRC), the conventional MGGP, multilayer perceptron neural network (MLPNN) and NARX, the INARX demonstrates the best forecast performance. Its mean coefficient of determination (CD) increases by 7.7%-38.6% and the root mean squared error (RMSE) reduces by 15.1%-54.5%. The INARX with the Coif3 wavelet is further evaluated for flood events and multistep forecast. Under flood conditions, the model generates satisfactory results, with CD > 0.83 and 84.7% of the simulated data falling within the ±0.1 kg/m3 error. For the multistep forecast, at a one-week lead time, the network also yields predictions with acceptable accuracy (mean CD = 0.78). The model performance deteriorates if the lead time becomes larger. The established framework is robust and reliable for real-time and multistep SSC forecast and provides reference for time series modeling, e.g. streamflow, river temperature and salinity.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 604, article id 127258
Keywords [en]
River suspended sediment, wavelet transformation, multigene genetic programing, multilayer perceptron neural network, INARX
National Category
Ocean and River Engineering Fluid Mechanics and Acoustics
Research subject
Fluid Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-87155DOI: 10.1016/j.jhydrol.2021.127258ISI: 000731346800003Scopus ID: 2-s2.0-85120692547OAI: oai:DiVA.org:ltu-87155DiVA, id: diva2:1595914
Note

Validerad;2022;Nivå 2;2022-01-01 (johcin)

Available from: 2021-09-20 Created: 2021-09-20 Last updated: 2022-06-01Bibliographically approved

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Xie, Qiancheng

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