Publication detail

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

UHLÍK, O.

Original Title

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

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

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.

Keywords

artificial neural network, agent-based model, evacuation

Authors

UHLÍK, O.

Released

20. 9. 2024

Pages count

9

URL

https://files.thunderheadeng.com/femtc/2024_pdf-archive.zip

BibTex

@inproceedings{BUT196480,
  author="Ondřej {Uhlík}",
  title="Real-time RSET Prediction Based on Simulation Dataset
Using Machine Learning: A Complex Geometry Case Study",
  year="2024",
  pages="9",
  url="https://files.thunderheadeng.com/femtc/2024_pdf-archive.zip"
}