 |
| Titel |
Prediction of littoral drift with artificial neural networks |
| VerfasserIn |
A. K. Singh, M. C. Deo, V. Sanil Kumar |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 12, no. 1 ; Nr. 12, no. 1 (2008-02-21), S.267-275 |
| Datensatznummer |
250010473
|
| Publikation (Nr.) |
 copernicus.org/hess-12-267-2008.pdf |
|
|
|
|
|
| Zusammenfassung |
| 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. |
| |
|
| Teil von |
|
|
|
|
|
|
|