Real-time RSET Prediction Based on Simulation Dataset Using Machine Learning: A Complex Geometry Case Study

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F24%3APU155388" target="_blank" >RIV/00216305:26110/24:PU155388 - isvavai.cz</a>
Result on the web
<a href="https://files.thunderheadeng.com/femtc/2024_pdf-archive.zip" target="_blank" >https://files.thunderheadeng.com/femtc/2024_pdf-archive.zip</a>
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Real-time RSET Prediction Based on Simulation Dataset Using Machine Learning: A Complex Geometry Case Study
Original language description
Agent-based evacuation model simulations are not suitable for real-time estimates due to their complexity and computational demands. Machine learning models allow for the approximation of simulations through estimates, creating a metamodel whose outputs can be used in real-time for effective decision-making in object safety management. The article presents a case study demonstrating the process of training the metamodel on a dataset with seven input features and simulations of evacuation model generated by a quasi-random sequence. Among the compared machine learning regression models, the ANN metamodel achieved the best results.
Czech name
—
Czech description
—

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)