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Analysis of climate indexes influence on wildfires using complex networks and data mining

Grant number: 17/05831-9
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): October 01, 2017
Effective date (End): May 31, 2020
Field of knowledge:Engineering - Electrical Engineering - Industrial Electronics, Electronic Systems and Controls
Principal researcher:Elbert Einstein Nehrer Macau
Grantee:Leonardo Nascimento Ferreira
Home Institution: Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia e Inovações (Brasil). São José dos Campos , SP, Brazil
Associated research grant:15/50122-0 - Dynamic phenomena in complex networks: basics and applications, AP.TEM
Associated scholarship(s):19/00157-3 - Association and causality analyses between climate and wildfires using network science, BE.EP.PD

Abstract

Wildfires are responsible for alterations in biodiversity, soil degradation, climate changes and put risk to human beings. In the last years, greenhouse gasses achieved an alarming rate in the atmosphere. Deforestation is the second largest anthropogenic cause of this increase and wildfires are one of the main methods. Observing this scenario, researchers have been conducting studies that try to forecast wildland fire. However, many of these investigations use traditional forecasting models and data mining techniques. These studies also usually use local data and might miss important information for the prediction. In this project, we intend to use climatological data to study the relationship between climate and wildfires using complex network theory and data mining. The main advantage of this approach is the capability of studying the interactions and dynamics between the small parts that compose a complex system. It may lead to scientific discoveries that are hardly detected by traditional techniques. After the network construction, we will adapt some pattern detection methods to the context of the project. The discovered patterns will be studied aiming at finding explanations for the wildfire intensity variations. These patterns will also serve as the basis for the creation of more accurate wildfire prediction methods. These new methods can be used to create better prevention policies, management, and control of wildland fire.

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
FERREIRA, LEONARDO N.; VEGA-OLIVEROS, DIDIER A.; COTACALLAPA, MOSHE; CARDOSO, MANOEL F.; QUILES, MARCOS G.; ZHAO, LIANG; MACAU, ELBERT E. N. Spatiotemporal data analysis with chronological networks. NATURE COMMUNICATIONS, v. 11, n. 1 AUG 12 2020. Web of Science Citations: 0.
FERREIRA, LEONARDO N.; VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; CARDOSO, MANOEL F.; MACAU, ELBERT E. N. Global fire season severity analysis and forecasting. Computers & Geosciences, v. 134, JAN 2020. Web of Science Citations: 1.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.