Method for Identifying Combustibles in Fires Based on Flame Spectroscopy Information

Abstract

Fires are one of the common disasters. If a fire occurs in a place with dense personnel and concentrated property, it will cause a large number of casualties and significant economic losses. Different types of objects can cause different fires, each requiring different extinguishing methods. This article aims to investigate the possibility of using flame spectroscopy to classify combustibles. Firstly, flame spectroscopy data of different combustibles are collected through experiments, with SG smoothing and Principal Component Analysis (PCA) selected as preprocessing methods. Subsequently, five machine learning algorithms are used for in-depth analysis and modeling, comparing the classification results of these five models to identify the model with the best classification performance. This study achieves the goal of classifying combustibles in fires using flame spectroscopy information.