A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications

Detalhes bibliográficos
Autor(a) principal: Leal, Philipe Riskalla
Data de Publicação: 2021
Outros Autores: Guimarães, Ricardo José de Paula Souza e, Cortivo, Fábio Dall, Palharini, Rayana Santos Araújo, Kampel, Milton
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Digital do Instituto Evandro Chagas (Patuá)
Texto Completo: https://patua.iec.gov.br/handle/iec/4455
Resumo: Detecting and predicting extreme events are of major importance for socioeconomic, healthcare and ecological purposes. This study proposes an alternative model to detect extreme events based on analyses of probability distribution functionffns s (), called Optimum Probability Distribution Function Searcher Model (Opt.PDF-model). The Opt.PDF-model involves the optimization of a fitness function between an histogram and a set of theoretical , and the subsequent evaluation of the Probability Point Function (PPF) of the fittest theoretical () to assess threshold values for the classification of extreme events. Any occurrence in the dataset with a PPF value equal to or greater than 90% was considered an extreme event candidate. A satellite-derived precipitation time-series (Climate Hazards Group InfraRed Precipitation with Station data) was used to calibrate and validate the proposed model, with data on accumulated precipitation from more than 30 years (Jan.1981 to Dec.2018) of the Brazilian Amazon region. The proposed method was pairwise cross-validated with two other extreme event models based on Gamma and Gaussian distributions, as applied by the European Drought Observatory of the European Environment Agency. Aditionally, all three extreme event classification models were cross-validated relative to the El Niño Southern Oscillation (ENSO). By means of the Opt.PDF-model, it was possible to evidence two positive temporal trends for the area of study: one for more intense precipitation events, and another for less intense events. The pairwise cross-validation analysis returned specific Kappa’s similarity indices, and the highest similarity was observed between the Gamma and the Opt.PDF models (48% for PPF(97.7%)). This analysis indicated that extreme event detection models are highly sensitive to distribution family priors and to threshold definitions. The proposed approach and the results obtained here are potentially useful for climate change warnings, and can be extended to other scientific areas that involve time-series analyses.
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spelling Leal, Philipe RiskallaGuimarães, Ricardo José de Paula Souza eCortivo, Fábio DallPalharini, Rayana Santos AraújoKampel, Milton2021-10-05T11:03:49Z2021-10-05T11:03:49Z2021LEAL, Philipe Riskalla et al. A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications. Remote Sensing Applications: Society and Environment, v. 24, n. 100618, Nov. 2021.2352-9385https://patua.iec.gov.br/handle/iec/445510.1016/j.rsase.2021.100618Detecting and predicting extreme events are of major importance for socioeconomic, healthcare and ecological purposes. This study proposes an alternative model to detect extreme events based on analyses of probability distribution functionffns s (), called Optimum Probability Distribution Function Searcher Model (Opt.PDF-model). The Opt.PDF-model involves the optimization of a fitness function between an histogram and a set of theoretical , and the subsequent evaluation of the Probability Point Function (PPF) of the fittest theoretical () to assess threshold values for the classification of extreme events. Any occurrence in the dataset with a PPF value equal to or greater than 90% was considered an extreme event candidate. A satellite-derived precipitation time-series (Climate Hazards Group InfraRed Precipitation with Station data) was used to calibrate and validate the proposed model, with data on accumulated precipitation from more than 30 years (Jan.1981 to Dec.2018) of the Brazilian Amazon region. The proposed method was pairwise cross-validated with two other extreme event models based on Gamma and Gaussian distributions, as applied by the European Drought Observatory of the European Environment Agency. Aditionally, all three extreme event classification models were cross-validated relative to the El Niño Southern Oscillation (ENSO). By means of the Opt.PDF-model, it was possible to evidence two positive temporal trends for the area of study: one for more intense precipitation events, and another for less intense events. The pairwise cross-validation analysis returned specific Kappa’s similarity indices, and the highest similarity was observed between the Gamma and the Opt.PDF models (48% for PPF(97.7%)). This analysis indicated that extreme event detection models are highly sensitive to distribution family priors and to threshold definitions. The proposed approach and the results obtained here are potentially useful for climate change warnings, and can be extended to other scientific areas that involve time-series analyses.Instituto Nacional de Pesquisas Espaciais. São Jose dos Campos, SP, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Laboratório de Geoprocessamento . Ananindeua, PA, Brasil.Instituto Nacional de Pesquisas Espaciais. São Jose dos Campos, SP, Brazil.Instituto Nacional de Pesquisas Espaciais. São Jose dos Campos, SP, Brazil.Instituto Nacional de Pesquisas Espaciais. São Jose dos Campos, SP, Brazil.engElsevierA new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applicationsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleAlerta em Desastres / métodosSistemas de Alerta / provisão & distribuiçãoPrecipitação AtmosféricaMudança ClimáticaDesastres / prevenção & controleEstudos de Séries TemporaisTecnologia de Sensoriamento Remoto / métodosinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Digital do Instituto Evandro Chagas (Patuá)instname:Instituto Evandro Chagas (IEC)instacron:IECORIGINALA new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications.pdfA new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications.pdfapplication/pdf551083https://patua.iec.gov.br/bitstreams/5683a42d-3a55-440d-9014-17d0a89babba/downloadc9a9c128e29cac82a5d7fdf3f4e6da73MD51LICENSElicense.txtlicense.txttext/plain; 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dc.title.pt_BR.fl_str_mv A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications
title A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications
spellingShingle A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications
Leal, Philipe Riskalla
Alerta em Desastres / métodos
Sistemas de Alerta / provisão & distribuição
Precipitação Atmosférica
Mudança Climática
Desastres / prevenção & controle
Estudos de Séries Temporais
Tecnologia de Sensoriamento Remoto / métodos
title_short A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications
title_full A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications
title_fullStr A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications
title_full_unstemmed A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications
title_sort A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications
author Leal, Philipe Riskalla
author_facet Leal, Philipe Riskalla
Guimarães, Ricardo José de Paula Souza e
Cortivo, Fábio Dall
Palharini, Rayana Santos Araújo
Kampel, Milton
author_role author
author2 Guimarães, Ricardo José de Paula Souza e
Cortivo, Fábio Dall
Palharini, Rayana Santos Araújo
Kampel, Milton
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Leal, Philipe Riskalla
Guimarães, Ricardo José de Paula Souza e
Cortivo, Fábio Dall
Palharini, Rayana Santos Araújo
Kampel, Milton
dc.subject.decsPrimary.pt_BR.fl_str_mv Alerta em Desastres / métodos
Sistemas de Alerta / provisão & distribuição
Precipitação Atmosférica
Mudança Climática
Desastres / prevenção & controle
Estudos de Séries Temporais
Tecnologia de Sensoriamento Remoto / métodos
topic Alerta em Desastres / métodos
Sistemas de Alerta / provisão & distribuição
Precipitação Atmosférica
Mudança Climática
Desastres / prevenção & controle
Estudos de Séries Temporais
Tecnologia de Sensoriamento Remoto / métodos
description Detecting and predicting extreme events are of major importance for socioeconomic, healthcare and ecological purposes. This study proposes an alternative model to detect extreme events based on analyses of probability distribution functionffns s (), called Optimum Probability Distribution Function Searcher Model (Opt.PDF-model). The Opt.PDF-model involves the optimization of a fitness function between an histogram and a set of theoretical , and the subsequent evaluation of the Probability Point Function (PPF) of the fittest theoretical () to assess threshold values for the classification of extreme events. Any occurrence in the dataset with a PPF value equal to or greater than 90% was considered an extreme event candidate. A satellite-derived precipitation time-series (Climate Hazards Group InfraRed Precipitation with Station data) was used to calibrate and validate the proposed model, with data on accumulated precipitation from more than 30 years (Jan.1981 to Dec.2018) of the Brazilian Amazon region. The proposed method was pairwise cross-validated with two other extreme event models based on Gamma and Gaussian distributions, as applied by the European Drought Observatory of the European Environment Agency. Aditionally, all three extreme event classification models were cross-validated relative to the El Niño Southern Oscillation (ENSO). By means of the Opt.PDF-model, it was possible to evidence two positive temporal trends for the area of study: one for more intense precipitation events, and another for less intense events. The pairwise cross-validation analysis returned specific Kappa’s similarity indices, and the highest similarity was observed between the Gamma and the Opt.PDF models (48% for PPF(97.7%)). This analysis indicated that extreme event detection models are highly sensitive to distribution family priors and to threshold definitions. The proposed approach and the results obtained here are potentially useful for climate change warnings, and can be extended to other scientific areas that involve time-series analyses.
publishDate 2021
dc.date.accessioned.fl_str_mv 2021-10-05T11:03:49Z
dc.date.available.fl_str_mv 2021-10-05T11:03:49Z
dc.date.issued.fl_str_mv 2021
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv LEAL, Philipe Riskalla et al. A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications. Remote Sensing Applications: Society and Environment, v. 24, n. 100618, Nov. 2021.
dc.identifier.uri.fl_str_mv https://patua.iec.gov.br/handle/iec/4455
dc.identifier.issn.-.fl_str_mv 2352-9385
dc.identifier.doi.-.fl_str_mv 10.1016/j.rsase.2021.100618
identifier_str_mv LEAL, Philipe Riskalla et al. A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data remote sensing applications. Remote Sensing Applications: Society and Environment, v. 24, n. 100618, Nov. 2021.
2352-9385
10.1016/j.rsase.2021.100618
url https://patua.iec.gov.br/handle/iec/4455
dc.language.iso.fl_str_mv eng
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