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International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472 EISSN: 1735-1472
Vol. 11, No. 4, 2014, pp. 1035-1042
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Bioline Code: st14102
Full paper language: English
Document type: Research Article
Document available free of charge
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International Journal of Environment Science and Technology, Vol. 11, No. 4, 2014, pp. 1035-1042
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Caspian Sea level prediction using satellite altimetry by artificial neural networks
Imani, M.; You, R.-J. & Kuo, C.-Y.
Abstract
The demand for accurate predictions of sea
level fluctuations in coastal management and ship navigation
activities is increasing. To meet such demand, accessible
high-quality data and proper modeling process are
critically required. This study focuses on developing and
validating a neural methodology applicable to the shortterm
forecast of the Caspian Sea level. The input and
output data sets used contain two time series obtained from
Topex/Poseidon and Jason-1 satellite altimetry missions
from 1993 to 2008. The forecast is performed by multilayer
perceptron network, radial basis function, and generalized
regression neural networks. Several tests of different artificial
neural network (ANN) architectures and learning
algorithms are carried out as alternative methods to the
conventional models to assess their applicability for estimating
Caspian Sea level anomalies. The results derived
from the ANN are compared with observed sea level values
and with the forecasts calculated by a routine autoregressive
moving average (ARMA) model. Different ANNs
satisfactorily provide reliable results for the short-term
prediction of Caspian Sea level anomalies. The root mean
square errors of the differences between observations and
predictions from artificial intelligence approaches can be
significantly reduced by about 50 % compared with
ARMA techniques.
Keywords
Artificial neural network; Sea level forecast; Caspian Sea; Satellite altimetry
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© International Journal of Environment Science and Technology Alternative site location: http://www.ijest.org
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