Multi-source heterogeneous data fusion prediction technique for the utility tunnel fire detection

Abstract

Diverse and complex fire environment in modern utility tunnels with multiple uncertainties make fire detection difficult to be achieved accurately. This study aims to develop an intelligent fire detection technique to address the difficulty. In the technique, initially, a lightweight image segmentation method is proposed for initial estimation of the fire source location. Then, the multi-source heterogeneous data fusion fire detection is implemented for fire source localization and ceiling temperature distribution prediction based on Gauss model and the improved multi-particle swarm optimization (MPSO) algorithm. Additionally, the results of the case study support the ability of the intelligent fire detection technique through compared with the experiment results and the previous methods, which can be used to achieve precise and stable fire source localization and ceiling temperature prediction in the utility tunnel fire.