Snow cover thickness estimation using radial basis function networks
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 classificati...
Published in: | The Cryosphere |
---|---|
Main Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Copernicus Publications
2013
|
Subjects: | |
Online Access: | https://doi.org/10.5194/tc-7-841-2013 http://www.the-cryosphere.net/7/841/2013/tc-7-841-2013.pdf https://doaj.org/article/49966ea980a24d1eb59d0b937c0ea0f2 |
id |
fttriple:oai:gotriple.eu:oai:doaj.org/article:49966ea980a24d1eb59d0b937c0ea0f2 |
---|---|
record_format |
openpolar |
spelling |
fttriple:oai:gotriple.eu:oai:doaj.org/article:49966ea980a24d1eb59d0b937c0ea0f2 2023-05-15T18:32:23+02:00 Snow cover thickness estimation using radial basis function networks E. Binaghi V. Pedoia A. Guidali M. Guglielmin 2013-05-01 https://doi.org/10.5194/tc-7-841-2013 http://www.the-cryosphere.net/7/841/2013/tc-7-841-2013.pdf https://doaj.org/article/49966ea980a24d1eb59d0b937c0ea0f2 en eng Copernicus Publications doi:10.5194/tc-7-841-2013 1994-0416 1994-0424 http://www.the-cryosphere.net/7/841/2013/tc-7-841-2013.pdf https://doaj.org/article/49966ea980a24d1eb59d0b937c0ea0f2 undefined The Cryosphere, Vol 7, Iss 3, Pp 841-854 (2013) geo psy Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2013 fttriple https://doi.org/10.5194/tc-7-841-2013 2023-01-22T18:10:33Z 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. Article in Journal/Newspaper The Cryosphere Unknown The Cryosphere 7 3 841 854 |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
English |
topic |
geo psy |
spellingShingle |
geo psy E. Binaghi V. Pedoia A. Guidali M. Guglielmin Snow cover thickness estimation using radial basis function networks |
topic_facet |
geo psy |
description |
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. |
format |
Article in Journal/Newspaper |
author |
E. Binaghi V. Pedoia A. Guidali M. Guglielmin |
author_facet |
E. Binaghi V. Pedoia A. Guidali M. Guglielmin |
author_sort |
E. Binaghi |
title |
Snow cover thickness estimation using radial basis function networks |
title_short |
Snow cover thickness estimation using radial basis function networks |
title_full |
Snow cover thickness estimation using radial basis function networks |
title_fullStr |
Snow cover thickness estimation using radial basis function networks |
title_full_unstemmed |
Snow cover thickness estimation using radial basis function networks |
title_sort |
snow cover thickness estimation using radial basis function networks |
publisher |
Copernicus Publications |
publishDate |
2013 |
url |
https://doi.org/10.5194/tc-7-841-2013 http://www.the-cryosphere.net/7/841/2013/tc-7-841-2013.pdf https://doaj.org/article/49966ea980a24d1eb59d0b937c0ea0f2 |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
The Cryosphere, Vol 7, Iss 3, Pp 841-854 (2013) |
op_relation |
doi:10.5194/tc-7-841-2013 1994-0416 1994-0424 http://www.the-cryosphere.net/7/841/2013/tc-7-841-2013.pdf https://doaj.org/article/49966ea980a24d1eb59d0b937c0ea0f2 |
op_rights |
undefined |
op_doi |
https://doi.org/10.5194/tc-7-841-2013 |
container_title |
The Cryosphere |
container_volume |
7 |
container_issue |
3 |
container_start_page |
841 |
op_container_end_page |
854 |
_version_ |
1766216488899837952 |