Forecasting Backdraft with Multimodal Method: Fusion of Fire Image and Sensor Data.

Experienced firefighters can fuse the flame image, smoke pattern, and varying temperature, sound, and odour in complex and fast-changing fire scenes to foresee flashover and explosion. This study mimics firefighters and proposes a novel transformer algorithm for the fusion of fire images and temperature sensor data to forecast the backdraft explosion in a building fire. The model of backdraft forecast is demonstrated with full-scale fire tests. After training 2674 fire scenarios with various fire intensities and images from various view angles, the FusionTransformer model can forecast the risk of backdraft with an overall accuracy of 84%. Moreover, the occurrence time and explosion scale of backdraft can be predicted with the Mean Absolute Error (MAE) of 1.6 s and 0.14 m, respectively. Compared with the single modal model, the fusion of fire images and temperature sensor data improves the accuracy of backdraft forecast by over 50%. This work demonstrates the use of a transformer algorithm in forecasting fire evolution and critical events. It also bridges the gap between data fusion methods and fire forecast, which inspires future universal AI-driven smart firefighting practices.