Evidential reasoning and lightweight multi-source heterogeneous data fusion-driven fire danger level dynamic assessment technique

Dynamic fire assessment under many uncertainties make danger level difficult to be evaluated reasonably. This investigation aims to establish a fire danger level dynamic assessment technique, which can be utilized to achieve automatically rules generation of fire assessment and multiple attributes-based fire danger level determination. In the technique, image segmentation with particle swarm optimization is used to generate the rules of fire assessment related to image type data automatically. Evidential reasoning with particle swarm optimization is used to generate the rules of fire assessment related to numeric type data automatically. Then, final fire danger level can be evaluated together with the help of the multi-source heterogeneous data. The main novelty of this investigation is that the developed multiple attributes-based assessment technique is not limited to the specific fire scenario, which is without resort to professional knowledges for determining the rules of fire assessment and training based on mass data in advance. In addition, the results of the case study of a tunnel fire support the ability of the technique, which can be used to achieve the reasonable fire danger level dynamic assessment with only the monitored data at the current time condition.