Missing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks

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...

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Autores principales:	Camilo Ocampo-Marulanda, Wilmar L. Cerón, Alvaro Avila-Diaz, Teresita Canchala, Wilfredo Alfonso-Morales, Mary T. Kayano, Roger R. Torres
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Complete missing data Reconstructs time series Extreme values of the indices ETCCDI indices NLPCA Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/263d51955934447ab2ba4693e8008b64
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spelling	oai:doaj.org-article:263d51955934447ab2ba4693e8008b642021-11-24T04:31:35ZMissing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks2352-340910.1016/j.dib.2021.107592https://doaj.org/article/263d51955934447ab2ba4693e8008b642021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352340921008672https://doaj.org/toc/2352-3409Changes 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.Camilo Ocampo-MarulandaWilmar L. CerónAlvaro Avila-DiazTeresita CanchalaWilfredo Alfonso-MoralesMary T. KayanoRoger R. TorresElsevierarticleComplete missing dataReconstructs time seriesExtreme values of the indicesETCCDI indicesNLPCAComputer applications to medicine. Medical informaticsR858-859.7Science (General)Q1-390ENData in Brief, Vol 39, Iss , Pp 107592- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Complete missing data Reconstructs time series Extreme values of the indices ETCCDI indices NLPCA Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
spellingShingle	Complete missing data Reconstructs time series Extreme values of the indices ETCCDI indices NLPCA Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 Camilo Ocampo-Marulanda Wilmar L. Cerón Alvaro Avila-Diaz Teresita Canchala Wilfredo Alfonso-Morales Mary T. Kayano Roger R. Torres Missing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks
description	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.
format	article
author	Camilo Ocampo-Marulanda Wilmar L. Cerón Alvaro Avila-Diaz Teresita Canchala Wilfredo Alfonso-Morales Mary T. Kayano Roger R. Torres
author_facet	Camilo Ocampo-Marulanda Wilmar L. Cerón Alvaro Avila-Diaz Teresita Canchala Wilfredo Alfonso-Morales Mary T. Kayano Roger R. Torres
author_sort	Camilo Ocampo-Marulanda
title	Missing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks
title_short	Missing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks
title_full	Missing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks
title_fullStr	Missing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks
title_full_unstemmed	Missing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks
title_sort	missing data estimation in extreme rainfall indices for the metropolitan area of cali - colombia: an approach based on artificial neural networks
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/263d51955934447ab2ba4693e8008b64
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Missing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks

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