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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Global fire season severity analysis and forecasting

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Author(s):
Ferreira, Leonardo N. [1] ; Vega-Oliveros, Didier A. [2, 3] ; Zhao, Liang [3] ; Cardoso, Manoel F. [4] ; Macau, Elbert E. N. [5, 1]
Total Authors: 5
Affiliation:
[1] Natl Inst Space Res, Associated Lab Comp & Appl Math, Sao Jose Dos Campos, SP - Brazil
[2] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN - USA
[3] Univ Sao Paulo, Dept Comp & Math, Ribeirao Preto, SP - Brazil
[4] Natl Inst Space Res, Ctr Earth Syst Sci, Cachoeira Paulista, SP - Brazil
[5] Univ Fed Sao Paulo, Inst Sci & Technol, Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 5
Document type: Journal article
Source: Computers & Geosciences; v. 134, JAN 2020.
Web of Science Citations: 1
Abstract

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. (AU)

FAPESP's process: 19/00157-3 - Association and causality analyses between climate and wildfires using network science
Grantee:Leonardo Nascimento Ferreira
Support type: Scholarships abroad - Research Internship - Post-doctor
FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support type: Research Projects - Thematic Grants
FAPESP's process: 17/05831-9 - Analysis of climate indexes influence on wildfires using complex networks and data mining
Grantee:Leonardo Nascimento Ferreira
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 16/23698-1 - Dynamical Processes in Complex Network based on Machine Learning
Grantee:Didier Augusto Vega Oliveros
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 18/01722-3 - Semi-supervised learning via complex networks: network construction, selection and propagation of labels and applications
Grantee:Lilian Berton
Support type: Regular Research Grants
FAPESP's process: 18/03211-6 - Non linear dynamics
Grantee:Iberê Luiz Caldas
Support type: Research Projects - Thematic Grants