Deep learning based inverse model for building fire source location and intensity estimation

Abstract Effective fire detection provides early warnings and key information for first responders and people trapped insides. The idea of integrating sensor data and fire modeling presents a general framework for fire source parameter estimation. However, most methods fail to achieve a real-time accurate estimation due to complex building structures and high computational requirements. Inspired by the capability of deep learning in data mining, a model based on Gated recurrent unit (GRU) is proposed to determine fire locations and intensity. First, a series of fire scenarios is simulated to form the dataset. Second, GRU is applied to learn representations from sensor data. Third, fire source parameters are estimated by the trained GRU with sequential sensor measurements. Multiple configurations and data are used to assess the inverse model. The results show that this model performs well and achieves a high test accuracy. The estimation of fire location is not influenced by the precision of fire simulations, while the intensity inversion is sensitive to the deviations. In addition, reliability, efficiency, and robustness of the inverse model are studied. This study is a fundamental step towards a credible and applicable deep learning-based model for fire source parameter inversion that assists in building fire protection.

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