Conference

A Computational Pipeline for Modeling and Predicting Wildfire Behavior

Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk, COMPLEXIS '23

Nuno Fachada — 2022 — SciTePress

Key information

Authors:

Nuno Fachada (Nuno Fachada)

Published in

April 2022

Abstract

Wildfires constitute a major socioeconomic burden. While a number of scientific and technological methods have been used for predicting and mitigating wildfires, this is still an open problem. In turn, agent-based modeling is a modeling approach where each entity of the system being modeled is represented as an independent decision-making agent. It is a useful technique for studying systems that can be modeled in terms of interactions between individual components. Consequently, it is an interesting methodology for modeling wildfire behavior. In this position paper, we propose a complete computational pipeline for modeling and predicting wildfire behavior by leveraging agent-based modeling, among other techniques. This project is to be developed in collaboration with scientific and civil stakeholders, and should produce an open decision support system easily extendable by stakeholders and other interested parties.

Publication details

Authors in the community:

Publication version

VoR - Version of Record

Publisher

SciTePress

Link to the publisher's version

https://doi.org/10.5220/0011073900003197

Title of the publication container

Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk, COMPLEXIS '23

Location of the conference

Virtual event

Conference date start

April 23, 2023

Conference date end

April 24, 2023

First page or article number

79

Last page

84

Volume

1

ISBN

978-989-758-565-4

ISSN

2184-5034

DOI

10.5220/0011073900003197

Fields of Science and Technology (FOS)

computer-and-information-sciences - Computer and information sciences

Keywords

  • Agent-based Modeling
  • High-performance Computing
  • Computational Intelligence
  • Verification and Validation
  • Wildfires

Publication language (ISO code)

eng - English

Rights type:

Open access

Financing entity

Fundação para a Ciência e a Tecnologia

Number for the project, award or grant: UIDB/04111/2020 (COPELABS)