# A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires
## 4 Flashover Occurrence Prediction
[12] using probabilistic mapping with maximum entropy (PEmap). It was employed as a binary classifier for predicting this phenomenon occurrence in single compartment fire and results were compared and verified against data obtained by Fuzzy ARTMAP (FAM). A computer package (FAST [53]) was used to introduce a flashover criterion (the temperature of the upper hot gas layer \(\geq\)600 \({}^{\circ}\)C) to predict flashover. Due to the impossibility of getting enough data of real emergencies, the training of the model was carried out through computer software simulations. Specifically, 375 simulations (190 samples of flashover and 185 non-flashover) were carried out to train the PEmap model, varying randomly different parameters of the compartment:  
* Length of the compartment (varies randomly from 2 to 10 m)
* Width of the compartment (varies randomly from 2 to 10 m)
* Height of the compartment (varies randomly from 2 to 10 m)
* Maximum heat release rate (varies randomly from 10 to 6000 kW)  
Subsequently, for different combinations of these room dimensions and the maximum heat release rate the occurrence of flashover was determined by FAST. Albeit the prediction results show that PEmap is an efficient classification tool for determination of flashover with a high degree of accuracy (96.8%), the neural network model was not implemented in any device, such as a thermal camera, to be tested in a real case. In a previous study [14] the GRNNFA model was applied to predict the thermal interface location in a single compartment fire. The performance of this ANN was proven to be comparable of the CFD model, moreover, computational speed of the GRNNFA was faster than CFD model. GRNNFApresented some limitations as it restricts the results in the application area of the model. In addition, [PERSON] et al. [15] presented a modification of the original GRNNFA model for multi-dimensional prediction problems and it was used to predict the velocity and temperature profiles at the centre of the doorway in a single compartment fire. GRNNFA model was successfully applied to predict the velocity and temperature at the centre of the doorway in a single compartment fire and multi-dimensional environment. There were some problems with the application of this technique in the local region because the training samples in that region may not be sufficient to describe the general behaviour of the system. Finally, this model was applied to predict the flashover under the given fire parameters (length, width, height and maximum heat release rate). It is concluded that this model is statistically superior than the Fuzzy ARTMAP and PEmap models [54]. In a real flashover situation, it is not possible to introduce parameters of a confined space in a device like TIC. The reason is that not only is the speed of events one of the crucial factors, but also the impossibility to know the exact dimensions of the enclosure. Image recognition techniques could be a useful technology to solve this problem. Another approach to predict the location of the thermal interface in a fire compartment (HTI) is studied in the work of [PERSON] et al. [55]. A neural network model based on probabilistic entropy (PENN) was used as an alternative to the CFD technique (computer simulation). Using this technology the network requires less computational time for a case study. PENN model is applied to the prediction of the HTI within a confined fire using real experimental data. The statistical analysis of the results positively demonstrates the accuracy of PENN model in a confined fire. Moreover, the computational time incurred in the prediction of the HTI is much better than CFD techniques. On the other hand, authors clarify that although the use of this technique is encouraging, new lines of research are needed to deepen the determination of the initial kernel radius, because these have been determined empirically for this study. In a fire emergency time is crucial to prevent a dangerous situation. As a consequence, predictions should be done in real time.