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A Computational Pipeline for Modeling and Predicting Wildfire Behavior

Topics: Big Data Predictive Modeling; Complex Engineering Problems, Simulations and Solutions; Data-Driven Models ; Disaster Management, Uncertainties and Modeling of Extreme Conditions; High Performance Computing for Risk; Models of Complex Networks; Population Models ; Risk Analysis and Management; Risk Assessment; Risk Minimization, Analytics and Deep Machine Learning; Simulation and Modeling

Author: Nuno Fachada

Affiliation: Lusófona University, COPELABS, Campo Grande, 376, Lisboa, Portugal

Keyword(s): Agent-based Modeling, High-performance Computing, Computational Intelligence, Verification and Validation, Wildfires.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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 - COMPLEXIS; ISBN 978-989-758-565-4; ISSN 2184-5034, SciTePress, pages 79-84. DOI: 10.5220/0011073900003197

@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 - COMPLEXIS},
year={2022},
pages={79-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011073900003197},
isbn={978-989-758-565-4},
issn={2184-5034},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS
TI - A Computational Pipeline for Modeling and Predicting Wildfire Behavior
SN - 978-989-758-565-4
IS - 2184-5034
AU - Fachada, N.
PY - 2022
SP - 79
EP - 84
DO - 10.5220/0011073900003197
PB - SciTePress

- Science and Technology Publications, Lda.
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