Smoke Particle-Source Prediction Model Based on Multiple Optical Wavelengths Using Deep Learning

Recently, installing smoke detectors has become crucial owing to the risk of fatal human damage that may be caused by inhaling smoke during a fire. Smoke detectors have been reported as highly efficient in detecting smoke particles from fire; however, they may generate false alarms because of their limitation in distinguishing the fire smoke from the smoke generated by daily activities. Despite the frequent occurrence of these false alarms, research on predicting the types of sources through smoke particles remains insufficient. This study involved the development process of an intelligent smoke detector for false alarm reduction that aims to predict the occurrence and type of fire and the evaluation of its performance using the light-scattering characteristics for fire/non-fire sources. First, a previous experimental dataset of fire-related conditions was collected from three fire sources and three non-fire sources to train the model with the light-scattering characteristics of the smoke generated from each source. In addition, to reduce the computing power, data preprocessing was performed on the collected dataset using the median and RobustScaler. Finally, we evaluated the prediction performance of the three deep learning models using three networks: RNN, LSTM, and CNN-LSTM. As a result, we confirmed that the scattering intensity of smoke particles has unique characteristics for each source. When the data preprocessing and prediction models were applied, all three models achieved an accuracy of 0.90 or higher. However, some errors occurred that appeared at similar scattering intensities. The proposed method differs from existing methods in that it presents the possibility of predicting fire and non-fire sources and can be used as an alternative for improving false alarms in the future.