Global fire season severity analysis and forecasting

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]}

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

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