A Hybrid Deep Learning Model for Generating Time-series Fire Data in Underground Utility Tunnel based on Convolutional Attention TimeGAN

Joseph Ahn, Hyo-gun Yoon

http://doi.org/10.5626/JOK.2024.51.6.490

Underground utility tunnels (UUTs) play a crucial role in urban operation and management. Fires are the most common disasters in the facilities, and there is a growing demand for fire management systems using artificial intelligence (AI). However, due to the difficulty of collecting fire data for AI training, utilizing data generation models reflecting the key characteristics of real fires can be an alternative. In this paper, we propose an approach for generating AI training data based on the fire data generation model CA-TimeGAN. To collect fire simulation data for training the proposed model, we constructed a UUT in Chungbuk Ochang within the fire dynamic simulator (FDS) virtual environment. In the experiments, we compared data generated by TimeGAN and CA-TimeGAN, verifying the data quality and effectiveness. Discriminative score converged to 0.5 for both CA-TimeGAN and TimeGAN. Predictive scores improved by 66.1% compared to models trained only on simulated data and by 22.9% compared to models incorporating TimeGAN-generated data. PCA and t-SNE analyses showed that the distribution of generated data was similar to that of simulated data.