Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire Monitoring

Outdoor fire detection faces significant challenges due to complex and variable environmental conditions. Fiber Optic Distributed Temperature Sensing (FO-DTS), recognized for its high sensitivity and broad monitoring range, provides significant advantages in detecting outdoor fires. However, predict...

Full description

Saved in:
Bibliographic Details
Main Authors: Haitao Bian, Xiaohan Luo, Zhichao Zhu, Xiaowei Zang, Yu Tian
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Fire
Subjects:
Online Access:https://www.mdpi.com/2571-6255/8/1/23
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832588484838162432
author Haitao Bian
Xiaohan Luo
Zhichao Zhu
Xiaowei Zang
Yu Tian
author_facet Haitao Bian
Xiaohan Luo
Zhichao Zhu
Xiaowei Zang
Yu Tian
author_sort Haitao Bian
collection DOAJ
description Outdoor fire detection faces significant challenges due to complex and variable environmental conditions. Fiber Optic Distributed Temperature Sensing (FO-DTS), recognized for its high sensitivity and broad monitoring range, provides significant advantages in detecting outdoor fires. However, prediction models trained in laboratory settings often yield false and missed alarms when deployed in complex outdoor settings, due to environmental interferences. To address this issue, this study developed a fixed-power fire source simulation device to establish a reliable small-scale experimental platform incorporating various environmental influences for generating anomalous temperature data. We employed deep learning autoencoders (AEs) to integrate spatiotemporal data, aiming to minimize the impact of outdoor conditions on detection performance. This research focused on analyzing how environmental temperature changes and rapid fluctuations affected detection capabilities, evaluating metrics such as detection accuracy and delay. Results showed that, compared to AE and VAE models handling spatial or temporal data, the CNN-AE demonstrated superior anomaly detection performance and strong robustness when applied to spatiotemporal data. Furthermore, the findings emphasize that environmental factors such as extreme temperatures and rapid temperature fluctuations can affect detection outcomes, increasing the likelihood of false alarms. This research underscores the potential of utilizing FO-DTS spatiotemporal data with CNN-AE for outdoor fire detection in complex scenarios and provides suggestions for mitigating environmental interference in practical applications.
format Article
id doaj-art-71490ed171f4447aab98a6c336618d90
institution Kabale University
issn 2571-6255
language English
publishDate 2025-01-01
publisher MDPI AG
record_format Article
series Fire
spelling doaj-art-71490ed171f4447aab98a6c336618d902025-01-24T13:32:19ZengMDPI AGFire2571-62552025-01-01812310.3390/fire8010023Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire MonitoringHaitao Bian0Xiaohan Luo1Zhichao Zhu2Xiaowei Zang3Yu Tian4College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, ChinaCollege of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, ChinaCollege of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, ChinaCollege of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, ChinaCollege of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, ChinaOutdoor fire detection faces significant challenges due to complex and variable environmental conditions. Fiber Optic Distributed Temperature Sensing (FO-DTS), recognized for its high sensitivity and broad monitoring range, provides significant advantages in detecting outdoor fires. However, prediction models trained in laboratory settings often yield false and missed alarms when deployed in complex outdoor settings, due to environmental interferences. To address this issue, this study developed a fixed-power fire source simulation device to establish a reliable small-scale experimental platform incorporating various environmental influences for generating anomalous temperature data. We employed deep learning autoencoders (AEs) to integrate spatiotemporal data, aiming to minimize the impact of outdoor conditions on detection performance. This research focused on analyzing how environmental temperature changes and rapid fluctuations affected detection capabilities, evaluating metrics such as detection accuracy and delay. Results showed that, compared to AE and VAE models handling spatial or temporal data, the CNN-AE demonstrated superior anomaly detection performance and strong robustness when applied to spatiotemporal data. Furthermore, the findings emphasize that environmental factors such as extreme temperatures and rapid temperature fluctuations can affect detection outcomes, increasing the likelihood of false alarms. This research underscores the potential of utilizing FO-DTS spatiotemporal data with CNN-AE for outdoor fire detection in complex scenarios and provides suggestions for mitigating environmental interference in practical applications.https://www.mdpi.com/2571-6255/8/1/23outdoor fire detectionanomaly temperature detectionfiber optic distributed temperature sensingspatiotemporal dataenvironmental interferences
spellingShingle Haitao Bian
Xiaohan Luo
Zhichao Zhu
Xiaowei Zang
Yu Tian
Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire Monitoring
Fire
outdoor fire detection
anomaly temperature detection
fiber optic distributed temperature sensing
spatiotemporal data
environmental interferences
title Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire Monitoring
title_full Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire Monitoring
title_fullStr Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire Monitoring
title_full_unstemmed Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire Monitoring
title_short Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire Monitoring
title_sort anomaly detection in spatiotemporal data from fiber optic distributed temperature sensing for outdoor fire monitoring
topic outdoor fire detection
anomaly temperature detection
fiber optic distributed temperature sensing
spatiotemporal data
environmental interferences
url https://www.mdpi.com/2571-6255/8/1/23
work_keys_str_mv AT haitaobian anomalydetectioninspatiotemporaldatafromfiberopticdistributedtemperaturesensingforoutdoorfiremonitoring
AT xiaohanluo anomalydetectioninspatiotemporaldatafromfiberopticdistributedtemperaturesensingforoutdoorfiremonitoring
AT zhichaozhu anomalydetectioninspatiotemporaldatafromfiberopticdistributedtemperaturesensingforoutdoorfiremonitoring
AT xiaoweizang anomalydetectioninspatiotemporaldatafromfiberopticdistributedtemperaturesensingforoutdoorfiremonitoring
AT yutian anomalydetectioninspatiotemporaldatafromfiberopticdistributedtemperaturesensingforoutdoorfiremonitoring