Enabling fire source localization in building fire emergencies with a machine learning-based inverse modeling approach

Abstract

During building fire emergency response (ER), one of the most important tasks for firefighting is to locate the fire source. Currently, incident commanders on the fire ground determine the origin of fire sources, relying mostly on the reports of firefighters entering the building or the information from on-site witnesses. This inevitably requires firefighters to approach the fire origin and observe, causing significant dangers and risks. To overcome this issue, an inverse model for building fire source localization is developed. A machine learning (ML)-based inverse modeling approach is used to build the complex relationship between the fire source location and on-site temperature sensor measurements, so that an inversion of the fire source based on the temperature observations can be enabled. By taking fire in an actual building floor as a case study, the inverse model for fire source localization using simulated fire temperature data is established. ML methods of feed-forward neural networks (FFNNs) and long short-term memory (LSTM) are respectively employed. Results indicate that LSTM in stateless mode is more appropriate to establish the relative models because more accurate recognition of the fire source room can be achieved during the initial and growth stages of fire development. Besides, by considering different means of on-site data acquisition in building fire emergencies, fire source localization via stationary temperature sensor arrays and portable temperature measuring devices is realized. The formulated models achieve recognition accuracy of 99.5% (direct recognition) and 92.1% (in top-3 candidates) in the first 15 min of the fires, respectively. The effectiveness of using the ML-based inverse modeling technique for fire source localization in building fire emergencies is demonstrated.