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dc.contributor.authorOcampo-Marulanda, Camilo
dc.contributor.authorCerón Loaiza, Wilmar
dc.contributor.authorAvila Diaz, Alvaro Javier
dc.contributor.authorCanchala Nastar, Teresita del Rocio
dc.contributor.authorAlfonso-Morales, Wilfredo
dc.contributor.authorKayano, Mary
dc.contributor.authorTorres, Roger R.
dc.coverage.spatialCali, Valle del Cauca, Colombia
dc.date.accessioned2022-02-14T15:40:37Z
dc.date.available2022-02-14T15:40:37Z
dc.date.issued2021
dc.identifier.citationOcampo-Marulanda C, Cerón WL, Avila-Diaz A, Canchala T, Alfonso-Morales W, Kayano MT, Torres RR. Missing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks. Data Brief. 2021 Nov 19;39:107592. doi: 10.1016/j.dib.2021.107592. PMID: 34869806; PMCID: PMC8626650.spa
dc.identifier.urihttps://repository.udca.edu.co/handle/11158/4462
dc.description.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.eng
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.esspa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/spa
dc.sourcehttps://pubmed.ncbi.nlm.nih.gov/34869806/#affiliation-5spa
dc.titleMissing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networksspa
dc.typeArtículo de revistaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)spa
dc.identifier.doihttps://doi.org/10.1016/j.dib.2021.107592
dc.subject.proposalNLPCAeng
dc.subject.proposalETCCDI indiceseng
dc.subject.proposalComplete missing dataeng
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
dc.subject.agrovocSistema climático
dc.subject.agrovocPrecipitación atmosférica
dc.relation.indexedN/Aspa
dc.relation.citationedition(Dic., 2021) Artículo número 107592spa
dc.relation.citationendpage10spa
dc.relation.citationstartpage1spa
dc.relation.citationvolume39spa
dc.relation.ispartofjournalData in Briefspa
dc.type.contentTextspa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa


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