A Computational Pipeline for Modeling and Predicting Wildfire Behavior

Nuno Fachada

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.

Paper Citation

in Harvard Style

Fachada N. (2022). A Computational Pipeline for Modeling and Predicting Wildfire Behavior. In Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS, ISBN 978-989-758-565-4, pages 79-84. DOI: 10.5220/0011073900003197

in Bibtex Style

@conference{complexis22,
author={Nuno Fachada},
title={A Computational Pipeline for Modeling and Predicting Wildfire Behavior},
booktitle={Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,},
year={2022},
pages={79-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011073900003197},
isbn={978-989-758-565-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,
TI - A Computational Pipeline for Modeling and Predicting Wildfire Behavior
SN - 978-989-758-565-4
AU - Fachada N.
PY - 2022
SP - 79
EP - 84
DO - 10.5220/0011073900003197