An Autoregressive Exogenous Neural Network to Model Fire Behavior via a Naïve Bayes Filter

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-stageclustering 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. INDEX TERMS Artificial neural networks, fire behaviour modelling, linear regression, self-organising feature maps.

Design and implementation of firefighting reconnaissance robot control system based on master-slave control

“…High temperatures and low visibility during a fire make finding evacuation routes difficult. While firefighters' PPE can withstand temperatures below 300°C, the actual fire often exceeds 500°C, endangering firefighter safety [2] . Thick smoke during a fire further complicates rescue efforts.…”

Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems

“…Over the past few years, building fires have been one of the most common and frequent types of fire, causing severe property losses and fatalities [ 1 , 2 , 3 ]. Thus, it has become extremely important and imperative to effectively decrease and control the risk of fire in buildings.…”

“…Sun et al [ 10 ] verified a bio-inspired artificial intelligence algorithm driven by temperature data to detect fire in 3D spaces. Yusuf et al [ 3 ] presented a linearly regressive artificial-neural-network-based technique to predict temperature increases caused by building fire environments. This method predicts temperature ranges in a burning compartment based on the historic fire behavior data modelled via a neural network algorithm.…”