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<p>Accurately understanding the spatiotemporal distribution of key hazardous factors in fire scenes assists firefighters in making more informed decisions. This study developed a hybrid deep learning framework combining Long Short-Term Memory networks (LSTM) and Convolutional Neural Networks (CNN) to quickly identify fire source locations and incorporate fire source spatial information into the spatiotemporal prediction of hazardous factors. For fire source location identification, under a 1-second sampling interval, the average absolute error of the sensor’s fire source coordinates was consistently below 0.1&#xa0;m. In the real-time monitoring of carbon monoxide, oxygen, and carbon dioxide, the CNN-LSTM model, after integrating non-temporal data such as fire source spatial information, significantly outperformed the baseline model in accuracy, achieving minimum normalized root mean square errors of 0.0333, 0.0402, and 0.0347, respectively. Moreover, in advance predictions up to 150&#xa0;s before the fire scene, the model maintained extremely high levels of accuracy. Sensitivity analysis of sensor performance found that the accuracy of fire prediction can be attributed to the sampling interval, and compact time-series data can significantly improve prediction accuracy. This study has important practical significance for firefighters in formulating more reliable rescue plans.</p>

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Towards Fire Digital Twin: Deep Learning Approach to Predicting Spatiotemporal Distribution of Fire Hazard Information

  • 期刊论文

  • 发表日期：

    2026年5月25日

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  • Zenghui Liu,
  • Guanhua Qu,
  • Ming Yan,
  • Lan Wang,
  • Xin Liu,
  • Gang Liu

摘要

Accurately understanding the spatiotemporal distribution of key hazardous factors in fire scenes assists firefighters in making more informed decisions. This study developed a hybrid deep learning framework combining Long Short-Term Memory networks (LSTM) and Convolutional Neural Networks (CNN) to quickly identify fire source locations and incorporate fire source spatial information into the spatiotemporal prediction of hazardous factors. For fire source location identification, under a 1-second sampling interval, the average absolute error of the sensor’s fire source coordinates was consistently below 0.1 m. In the real-time monitoring of carbon monoxide, oxygen, and carbon dioxide, the CNN-LSTM model, after integrating non-temporal data such as fire source spatial information, significantly outperformed the baseline model in accuracy, achieving minimum normalized root mean square errors of 0.0333, 0.0402, and 0.0347, respectively. Moreover, in advance predictions up to 150 s before the fire scene, the model maintained extremely high levels of accuracy. Sensitivity analysis of sensor performance found that the accuracy of fire prediction can be attributed to the sampling interval, and compact time-series data can significantly improve prediction accuracy. This study has important practical significance for firefighters in formulating more reliable rescue plans.