Multi-parameter fire detection method based on feature depth extraction and stacking ensemble learning model

The losses caused by fire are becoming more and more serious, with the continuous development of social economy. If fire is detected as early as possible, the losses can be reduced more than 80%. The detection accuracy of traditional single-parameter fire detection is low, which often leads to false and missing fire alarm. This paper proposes a multi-parameter fire detection method based on feature depth extraction and stacking ensemble learning model. Fire feature parameters, such as temperature, smoke concentration and CO concentration, are collected as the original data. The algorithms, based on XGboost, gradient boosted decision tree (GBDT), random forest and decision tree, are used to analyze and sort the importance of nine types of data, such as the slope of smoke concentration, the amplitude of CO concentration etc. Three types of data with high importance are selected to extract depth features using Hilbert Huang transform (HHT), one-dimensional convolution neural network (1D-CNN) and long short term memory neural network (LSTMNN). The extracted features are input into stacking ensemble learning model. Stacking ensemble learning model is a hierarchical combination learning classifier and improves the performance by learning the classification bias of the upper model. Experimental results show that the proposed method has higher accuracy, stability and efficiency against several single models (e.g., XGBoost, GBDT, and LSTMNN).