@Article{OcampoMarulandaCeAvCaAlKaTo:2021:ApBaAr,
               author = "Ocampo Marulanda, Camilo and Cer{\'o}n, Wilmar L. and Avila Diaz, 
                         Alvaro and Canchala, Tersita and Alfonso Morales, Wilfredo and 
                         Kayano, Mary Toshie and Torres, Roger R.",
          affiliation = "{Fundaci{\'o}n Universitaria de San Gil} and {Universidad del 
                         Valle} and {Universidad de Ciencias Aplicadas y Ambientale} and 
                         {Universidad del Valle} and {Universidad del Valle} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de Itajub{\'a} (UNIFEI)}",
                title = "Missing data estimation in extreme rainfall indices for the 
                         Metropolitan area of Cali - Colombia: an approach based on 
                         artificial neural networks",
              journal = "Data in Brief",
                 year = "2021",
               volume = "39",
                pages = "e107592",
                month = "Dec.",
             keywords = "Complete missing data, ETCCDI indices, Extreme values of the 
                         indices, NLPCA, Reconstructs time series.",
             abstract = "Changes 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.",
                  doi = "10.1016/j.dib.2021.107592",
                  url = "http://dx.doi.org/10.1016/j.dib.2021.107592",
                 issn = "2352-3409",
             language = "en",
           targetfile = "ocamp_missing.pdf",
        urlaccessdate = "21 maio 2024"
}