We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Applying Machine Learning for Firebrand Production Prediction.
- Authors
Jha, Anurag; Zhou, Aixi
- Abstract
This article presents a machine learning (ML) based metamodeling framework for firebrand production prediction. This framework was implemented to predict the firebrand areal mass density (FAMD) and firebrand areal number density (FAND) of landing firebrands using a large set of data from full-scale laboratory firebrand production experiments. The independent variables used in our ML models to predict the dependent variables FAND and FAMD were landing (or travel) distance, wind speed, and fuel type (structural and vegetative fuels). It was demonstrated that the non-linear non-parametric ML model, K-nearest neighbors (KNN), works the best for this purpose. The KNN model predicted discrete FAND and FAMD values with an accuracy higher than 90%. The current ML model can be used to predict locations with high risk of spotting ignition potential. This research is a small step towards the bigger goal of creating a numerical firebrand production simulator.
- Subjects
K-nearest neighbor classification; WIND speed; MACHINE learning
- Publication
Fire Technology, 2022, Vol 58, Issue 5, p3261
- ISSN
0015-2684
- Publication type
Article
- DOI
10.1007/s10694-022-01309-z