Fachada, Nuno2022-04-282022-04-282022-04-24Fachada, 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 2022 (pp. 79-84), Virtual Event. SciTePress/INSTICC.https://doi.org/10.5220/0011073900003197http://hdl.handle.net/10437/12850COMPLEXIS 2022 - 7th International Conference on Complexity, Future Information Systems and RiskWildfires 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. Keywords: Agent-based Modeling, High-performance Computing, Computational Intelligence, Verification and Validation, Wildfires.application/pdfengopenAccessINFORMÁTICACOMPUTAÇÃOCOMPUTAÇÃO DE ALTO DESEMPENHOMODELAÇÃO BASEADA EM AGENTESINCÊNDIOSCOMPUTER SCIENCECOMPUTATIONHIGH-PERFORMANCE COMPUTINGAGENT-BASED MODELINGFIRESconferenceObject