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Association and causality analyses between climate and wildfires using network science

Grant number: 19/00157-3
Support Opportunities:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): April 01, 2019
Effective date (End): March 31, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Elbert Einstein Nehrer Macau
Grantee:Leonardo Nascimento Ferreira
Supervisor: Jurgen Kurths
Host Institution: Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia e Inovações (Brasil). São José dos Campos , SP, Brazil
Research place: Humboldt University, Germany  
Associated to the scholarship:17/05831-9 - Analysis of climate indexes influence on wildfires using complex networks and data mining, BP.PD

Abstract

Wildfires are part of a complex system formed by climate, vegetation, and human factors. The fire changes the vegetation and soil, destroys buildings, affects the biodiversity, and influences human activities. Recent studies project that we will face more severe wildfires in the next years and that the traditional fire forecasting methods might be ineffective. Given this pessimist scenario and the high complexity involving fire dynamics, we need better models to study global and regional fire activity. In the last decade, complex networks have emerged as a powerful model for the analysis of complex systems. This model permits the quantification of the features and dynamics of the system in different scales. In the context of climate sciences, complex networks have been used to study many climatological phenomena, but not wildfires. Given the potential of this tool and the complexity of global fire dynamics, the goal of this project is to develop network-based methods to quantify the influence of the climate on fire activity. We will focus on temporal climate networks constructed using two approaches: statistical association measures (correlation networks) and causal inference (causal effect networks). In both cases, we will divide the underlying region into grid cells and use nodes to represent them. The major difference is the way how the links between nodes are established. First, we want to quantify the impacts of climatological phenomena, like El-Niño and extreme events, in the network topologies. Then we will search for temporal patterns in the network topologies using graph mining tools. We expect to better understand the relationship between climate and fire.

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Scientific publications (7)
(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)
GOUVEA, ALESSANDRA M. M. M.; DA SILVA, TIAGO S.; MACAU, ELBERT E. N.; QUILES, MARCOS G.. Force-directed algorithms as a tool to support community detection. European Physical Journal-Special Topics, . (19/00157-3, 17/05831-9, 15/50122-0, 19/26283-5, 16/16291-2, 16/23698-1, 16/23642-6)
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, . (13/07375-0, 17/05831-9, 18/24260-5, 16/23698-1, 16/16291-2, 15/50122-0, 19/00157-3, 19/26283-5)
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, . (13/07375-0, 17/05831-9, 16/23698-1, 15/50122-0, 19/00157-3, 18/03211-6, 18/01722-3)
GOUVEA, ALESSANDRA M. M. M.; RUBIDO, NICOLAS; MACAU, ELBERT E. N.; QUILES, MARCOS G.. Importance of Numerical Implementation and Clustering Analysis in Force-Directed Algorithms for Accurate Community Detection. Applied Mathematics and Computation, v. 431, p. 21-pg., . (19/26283-5, 17/05831-9, 16/23698-1, 16/16291-2, 19/00157-3)
GOUVEA, ALESSANDRA M. M. M.; VEGA-OLIVEROS, DIDIER A.; COTACALLAPA, MOSHE; FERREIRA, LEONARDO N.; MACAU, ELBERT E. N.; QUILES, MARCOS G.; GERVASI, O; MURGANTE, B; MISRA, S; GARAU, C; et al. Dynamic Community Detection into Analyzing of Wildfires Events. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT I, v. 12249, p. 16-pg., . (16/16291-2, 19/26283-5, 17/05831-9, 19/00157-3, 15/50122-0, 16/23698-1, 16/23642-6)
GOUVEA, ALESSANDRA M. M. M.; DA SILVA, TIAGO S.; MACAU, ELBERT E. N.; QUILES, MARCOS G.. Force-directed algorithms as a tool to support community detection. European Physical Journal-Special Topics, v. 230, n. 14-15, p. 2745-2763, . (16/16291-2, 19/26283-5, 16/23698-1, 17/05831-9, 19/00157-3, 16/23642-6, 15/50122-0)
COTACALLAPA, MOSHE; BERTON, LILIAN; FERREIRA, LEONARDO N.; QUILES, MARCOS G.; ZHAO, LIANG; MACAU, ELBERT E. N.; VEGA-OLIVEROS, DIDIER A.; IEEE. Measuring the engagement level in encrypted group conversations by using temporal networks. 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., . (17/05831-9, 16/23698-1, 16/16291-2, 18/24260-5, 15/50122-0, 18/01722-3, 19/00157-3)

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