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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/45SHTJH
Repositorysid.inpe.br/mtc-m21d/2021/11.29.13.10   (restricted access)
Last Update2021:11.29.13.10.23 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2021/11.29.13.10.23
Metadata Last Update2022:04.03.22.27.43 (UTC) administrator
DOI10.1016/j.dib.2021.107592
ISSN2352-3409
Citation KeyOcampoMarulandaCeAvCaAlKaTo:2021:ApBaAr
TitleMissing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: an approach based on artificial neural networks
Year2021
MonthDec.
Access Date2024, May 19
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size2999 KiB
2. Context
Author1 Ocampo Marulanda, Camilo
2 Cerón, Wilmar L.
3 Avila Diaz, Alvaro
4 Canchala, Tersita
5 Alfonso Morales, Wilfredo
6 Kayano, Mary Toshie
7 Torres, Roger R.
Resume Identifier1
2
3
4
5
6 8JMKD3MGP5W/3C9JHST
Group1
2
3
4
5
6 DIMNT-CGCT-INPE-MCTI-GOV-BR
Affiliation1 Fundación Universitaria de San Gil
2 Universidad del Valle
3 Universidad de Ciencias Aplicadas y Ambientale
4 Universidad del Valle
5 Universidad del Valle
6 Instituto Nacional de Pesquisas Espaciais (INPE)
7 Universidade Federal de Itajubá (UNIFEI)
Author e-Mail Address1 camilo.ocampo@correounivalle.edu.co
2
3
4
5
6 mary.kayano@inpe.br
JournalData in Brief
Volume39
Pagese107592
History (UTC)2021-11-29 13:11:25 :: simone -> administrator :: 2021
2022-04-03 22:27:43 :: administrator -> simone :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsComplete missing data
ETCCDI indices
Extreme values of the indices
NLPCA
Reconstructs time series
AbstractChanges observed in the current climate and projected for the future significantly concern researchers, decision-makers, and the general public. Climate indices of extreme rainfall events are a trend assessment tool to detect climate variability and change signals, which have an average reliability at least in the short term and given climatic inertia. This paper shows 12 climate indices of extreme rainfall events for annual and seasonal scales for 12 climate stations between 1969 to 2019 in the Metropolitan area of Cali (southwestern Colombia). The construction of the indices starts from daily rainfall time series, which although have between 0.5% and 5.4% of missing data, can affect the estimation of the indices. Here, we propose a methodology to complete missing data of the extreme event indices that model the peaks in the time series. This methodology uses an artificial neural network approach known as Non-Linear Principal Component Analysis (NLPCA). The approach reconstructs the time series by modulating the extreme values of the indices, a fundamental feature when evaluating extreme rainfall events in a region. The accuracy in the indices estimation shows values close to 1 in the Pearson's Correlation Coefficient and in the Bi-weighting Correlation. Moreover, values close to 0 in the percent bias and RMSE-observations standard deviation ratio. The database provided here is an essential input in future evaluation studies of extreme rainfall events in the Metropolitan area of Cali, the third most crucial urban conglomerate in Colombia with more than 3.9 million inhabitants.
AreaMET
Arrangementurlib.net > CGCT > Missing data estimation...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 29/11/2021 10:10 1.0 KiB 
4. Conditions of access and use
Languageen
Target Fileocamp_missing.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KUATE
Citing Item Listsid.inpe.br/bibdigital/2022/04.03.22.23 2
sid.inpe.br/mtc-m21/2012/07.13.14.56.22 1
DisseminationPORTALCAPES; SCOPUS.
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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