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A Hybrid Deep Learning Model for Generating Time-series Fire Data in Underground Utility Tunnel based on Convolutional Attention TimeGAN
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.
A Deep Learning Model for Fire Anomaly Detection in Underground Utility Tunnel based on ConvLSTM Variational AutoEncoder
http://doi.org/10.5626/JOK.2024.51.4.333
As the failure of fire detection not only leads to an escalation in disaster management costs but also inflicts significant damages and disruptions to citizens" lives and industries, accurate detection of fire anomalies is of paramount importance. There have been several studies on monitoring and managing catastrophic events using AI, IoT and digital twin technologies. However, the challenges arise from the telecommunications environment and the level of sensor maintenance, making it difficult for IoT sensors to collect data without experiencing loss or noise. This paper proposes a hybrid deep learning model called ConvLSTM-VAE that can detect anomalies by considering spatial and temporal information simultaneously, demonstrating robust results even in the presence of noise or data loss. A virtual environment modeled after the underground utility tunnel located in Ochang, Chungcheongbuk-do is constructed to collect fire data using Fire Dynamics Simulator (FDS) software. In the experiment we compared the proposed model to other time-series anomaly detection models and evalutated its predictive performance. The results show that the precision, recall, accuracy, and F1-score of ConvLSTM-VAE are 0.881579, 0.99505, 0.930693, and 0.934884, respectively, and far superior to other models in terms of its predictive performance.
A Hybrid Deep Learning Model for Real-Time Forecasting Fire Temperature in Underground Utility Tunnel Based on Residual CNN-LSTM
http://doi.org/10.5626/JOK.2024.51.2.131
Underground utility tunnels (UUTs) play major roles in sustaining the life of citizens and industries with regard to carrying electricity, telecommunication, water supply pipes. Fire is one of the most commonly common disasters in underground facilities, which can be prevented through proper management. This paper proposes a hybrid deep learning model named Residual CNN-LSTM to predict fire temperatures. Scenarios of underground facility fire outbreaks were created and fire temperature data was collected using FDS software. In the experiment, we analyzed the appropriate depth of residual learning of the proposed model and compared the performance to other predictive models. The results showed that RMSE, MAE and MAPE of Residual CNN-LSTM are each 0.061529, 0.053851, 6.007076 respectively, making Residual CNN-LSTM far superior to other models in terms of its predictive performance.
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