%0 Journal Article
%@nexthigherunit 8JMKD3MGPCW/3ESGTTP
%@nexthigherunit 8JMKD3MGPCW/3F2PHGS
%@nexthigherunit 8JMKD3MGPCW/3F3T29H
%@resumeid
%@resumeid
%@resumeid
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JGUT
%X Fire activity has a huge impact on human lives. Different models have been proposed to predict fire activity, which can be classified into global and regional ones. Global fire models focus on longer timescale simulations and can be very complex. Regional fire models concentrate on seasonal forecasting but usually require inputs that are not available in many places. Motivated by the possibility of having a simple, fast, and general model, we propose a seasonal fire prediction methodology based on time series forecasting methods. It consists of dividing the studied area into grid cells and extracting time series of fire counts to fit the forecasting models. We apply these models to estimate the fire season severity (FSS) from each cell, here defined as the sum of the fire counts detected in a season. Experimental results using a global fire detection data set show that the proposed approach can predict FSS with a relatively low error in many regions. The proposed approach is reasonably fast and can be applied on a global scale.
%8 Jan.
%9 journal article
%T Global fire season severity analysis and forecasting
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress manoel.cardoso@inpe.br
%@electronicmailaddress elbert.macau@inpe.br
%K Global fire activity, Wildfire, Fire season length, Fire severity, Climate change, Time series prediction.
%@secondarytype PRE PI
%@archivingpolicy denypublisher denyfinaldraft24
%@usergroup simone
%@group CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
%@group
%@group
%@group COCST-COCST-INPE-MCTIC-GOV-BR
%@group LABAC-COCTE-INPE-MCTIC-GOV-BR
%3 ferreira_global.pdf
%@secondarymark A1_GEOGRAFIA A1_ENGENHARIAS_II A1_ENGENHARIAS_I A2_INTERDISCIPLINAR A2_ENGENHARIAS_III A2_CIÊNCIAS_AMBIENTAIS A2_CIÊNCIA_DA_COMPUTAÇÃO B1_SAÚDE_COLETIVA B1_GEOCIÊNCIAS B1_BIODIVERSIDADE B2_CIÊNCIAS_BIOLÓGICAS_II
%@issn 0098-3004
%2 sid.inpe.br/mtc-m21c/2020/01.02.14.19.04
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Indiana University
%@affiliation Universidade de São Paulo (USP)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%B Computers and Geosciences
%@versiontype publisher
%P UNSP 104339
%4 sid.inpe.br/mtc-m21c/2020/01.02.14.19
%@documentstage not transferred
%D 2020
%V 134
%@doi 10.1016/j.cageo.2019.104339
%A Ferreira, Leonardo N.,
%A Vega-Oliveros, Didier A.,
%A Zhao, Liang,
%A Cardoso, Manoel Ferreira,
%A Macau, Elbert Einstein Nehrer,
%@dissemination WEBSCI; PORTALCAPES; COMPENDEX; SCOPUS.
%@area COMP
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}