 |
| Titel |
Snow cover thickness estimation using radial basis function networks |
| VerfasserIn |
E. Binaghi, V. Pedoia, A. Guidali, M. Guglielmin |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1994-0416
|
| Digitales Dokument |
URL |
| Erschienen |
In: The Cryosphere ; 7, no. 3 ; Nr. 7, no. 3 (2013-05-14), S.841-854 |
| Datensatznummer |
250017969
|
| Publikation (Nr.) |
 copernicus.org/tc-7-841-2013.pdf |
|
|
|
|
|
| Zusammenfassung |
| This paper reports an experimental study designed for the in-depth
investigation of how the radial basis function network (RBFN) estimates snow
cover thickness as a function of climate and topographic parameters. The
estimation problem is modeled in terms of both function regression and
classification, obtaining continuous and discrete thickness values,
respectively. The model is based on a minimal set of climatic and topographic
data collected from a limited number of stations located in the Italian
Central Alps. Several experiments have been conceived and conducted adopting
different evaluation indexes. A comparison analysis was also developed for a
quantitative evaluation of the advantages of the RBFN method over to
conventional widely used spatial interpolation techniques when dealing with
critical situations originated by lack of data and limited n-homogeneously
distributed instrumented sites. The RBFN model proved competitive behavior
and a valuable tool in critical situations in which conventional techniques
suffer from a lack of representative data. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|