Daily suspended sediment forecast by an integrated dynamic neural network
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{LI:2022:JH,
-
author = "Shicheng Li and Qiancheng Xie and James Yang",
-
title = "Daily suspended sediment forecast by an integrated
dynamic neural network",
-
journal = "Journal of Hydrology",
-
volume = "604",
-
pages = "127258",
-
year = "2022",
-
ISSN = "0022-1694",
-
DOI = "doi:10.1016/j.jhydrol.2021.127258",
-
URL = "https://www.sciencedirect.com/science/article/pii/S0022169421013081",
-
keywords = "genetic algorithms, genetic programming, River
suspended sediment, Wavelet transformation, Multigene
genetic programing, Multilayer perceptron neural
network, INARX",
-
abstract = "Suspended sediment is of importance in river and dam
engineering. 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
(thereafter 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 the 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 7.7percent-38.6percent and the root mean
squared error (RMSE) reduces by
15.1percent-54.5percent. The INARX with the Coif3
wavelet is further evaluated for flood events and
multistep forecasts. Under flood conditions, the model
generates satisfactory results, with CD > 0.83 and
84.7percent of the simulated data falling within the
plus-minus0.1 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 forecasts and
provides reference for time series modeling, e.g.
streamflow, river temperature and salinity",
- }
Genetic Programming entries for
Shicheng Li
Qiancheng Xie
James Yang
Citations