# A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires
## 4 Flashover Occurrence Prediction  
Regarding the prediction of these phenomenon, most of the relevant studies shown below have been carried out using neural networks. Moreover, ANN has been used in research cited in this review to predict some physical aspects related to fires, even to predict the flashover phenomenon but not using TIC. Research to perform flame detection using neural networks was conducted by [PERSON] et al. [8; 9]. Joint time-frequency analysis (JTFA) was used with a reduced time Fourier transform to extract relevant information. This study focused on the domain of the signal frequency, since the signals that a fire source emit can vary in amplitude and function depending on the distance, angle, presence of obstacles and other sources that are not flame generators (sun, wind, random modulation, bright lights, rain, fog, dust, etc.) that may look like flames in the eyes of an infrared sensor. In contrast to the time domain, the frequency of flickering of a flame remains relatively independent of the different environmental conditions. Due to the amount of several false positives that can occur, it was considered to use a multiple neural network instead of a large scale. This model was mounted on a digital signal processor (DSP, Texas Instruments (TI) F2812), reaching  
Figure 4: Image transformation. the conclusion that the response to the detection of a flame of this model is equal to or greater than some of the current detection systems. Another approach is the one given by [PERSON] et al. [52]. His research is based on prediction by studying the neighbouring pixels of a video, provided by a TIC. The author proposes a flame detection system based on an algorithm optimised with a digital signal processor to optimise detection time, between 5 and 20 s.  
Predicting parameters in compartment fires is an extremely fast alternative approach. In this line, several ANN's have been developed to do it instead of CFD techniques, which need more computational time to do the same work. In these cases, CFD techniques were used to validate ANN. In the following research, a novel ANN model GRNNFA, which is a fusion of the Fuzzy Adaptive Resonance Theory (FA) model and the General Regression Neural Network (GRNN) model, was developed to predict the locations of the thermal interfaces by [PERSON] et al. [14]. To train and evaluate the ANN, experimental data obtained from [PERSON] et al. [24] were employed in a total of 55 experiments conducted by [PERSON] (with different fire locations, fire intensities and window and door sizes). Due to the diffusion and mixing effects of the fluid flow, the thermal height could not be determined precisely. For these cases, FDS was used to validate the ANN performing a simulation for the experimental data. When the simulations were compared with the experimental data, the authors noticed that FDS consistently under-predicted the experimental results of the thermal interface height. For this reason, the absolute difference was averaged, and this correction factor was applied for the neural network training. The predicted results by GRNNFA of the 55 cases were compared with the simulations results. It was found that the GRNNFA was able to predict the locations of the thermal interfaces well within the range envelope of the experimental results up to 94.5% accuracy (i.e., only 3 out of 55 samples was predicted outside the range envelope). The previous model was applied to five test cases that not appear in the experiments and the results were compared to FDS simulations. Subsequently, it was found that the difference between the predictions and simulations are well within the minimum error range. The key issue here is GRNNFA was able to capture efficiently the genuine, predominant characteristics of the fire phenomenon within seconds from limited samples. This ANN was applied to predict the flashover phenomenon in enclosure fires [13] based on the compartment geometry. Regarding flashover prediction, a new ANN model was developed by [PERSON] et al.