Abstract
This work presents an artificial neural network-based linearly regressive technique for the prediction of a temperature rise event caused by a fire in enclosed building environments. The method predicts temperature range in a burning compartment based on the historic fire behavior data modelled via a neural network algorithm. The approach further extends the method by transforming the regression outcome as actionable information for firefighters via a self-organising feature map (SOM) fire-stage-clustering algorithm which categorises the predicted temperature range to provide warnings of any imminent and catastrophic temperature variations. The methodology implements a linear regression artificial neural model to model and predicts catastrophic temperature variations that are a threat to fire-and-rescue personnel in enclosed burning compartments via a ceiling gas-layer temperature acting as an exogenous variable. Based on the predicted temperature window, the SOM classifier maps the output in distinct clusters while isolating spurious prediction spikes and categorising the output to various self-organising threat/warning levels. The model was trained on temperature data captured with pole-mounted sensors in controlled fire training exercises. Tests carried out in actual fire-settings showed the capability of the SOM classifier to generate evacuation warnings for forecasted temperatures exceeding 300 C in 5 - 30 seconds ahead of the occurrences. Tests on these short-term and long-term prediction ranged from 78.31% to 96.27% in accuracy whereas the SOM classifier generated an overall accuracy of 76.92% to 100%. The techniques also showed a high resilience against both false-positives and false-negatives.