Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.153
IF5.153
IF 5-year value: 5.460
IF 5-year
5.460
CiteScore value: 7.8
CiteScore
7.8
SNIP value: 1.623
SNIP1.623
IPP value: 4.91
IPP4.91
SJR value: 2.092
SJR2.092
Scimago H <br class='widget-line-break'>index value: 123
Scimago H
index
123
h5-index value: 65
h5-index65
Volume 12, issue 1
Hydrol. Earth Syst. Sci., 12, 267–275, 2008
https://doi.org/10.5194/hess-12-267-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Data-driven approaches, optimization and model integration:...

Hydrol. Earth Syst. Sci., 12, 267–275, 2008
https://doi.org/10.5194/hess-12-267-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Feb 2008

21 Feb 2008

Prediction of littoral drift with artificial neural networks

A. K. Singh1, M. C. Deo1, and V. Sanil Kumar2 A. K. Singh et al.
  • 1Department of Civil Engineering, Indian Institute of Technology, Bombay, Mumbai 400 076, India
  • 2Ocean Engineering, National Institute of Oceanography, Dona Paula, Goa 403 004, India

Abstract. The amount of sand moving parallel to a coastline forms a prerequisite for many harbor design projects. Such information is currently obtained through various empirical formulae. Despite so many works in the past an accurate and reliable estimation of the rate of sand drift has still remained as a problem. The current study addresses this issue through the use of artificial neural networks (ANN). Feed forward networks were developed to predict the sand drift from a variety of causative variables. The best network was selected after trying out many alternatives. In order to improve the accuracy further its outcome was used to develop another network. Such simple two-stage training yielded most satisfactory results. An equation combining the network and a non-linear regression is presented for quick field usage. An attempt was made to see how both ANN and statistical regression differ in processing the input information. The network was validated by confirming its consistency with underlying physical process.

Publications Copernicus
Download
Citation