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

Fire, Год журнала: 2025, Номер 8(1), С. 23 - 23

Опубликована: Янв. 10, 2025

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 broad monitoring range, provides advantages in detecting outdoor fires. However, prediction models trained laboratory settings often yield false missed alarms when deployed settings, interferences. To address this issue, study developed a fixed-power source simulation device establish reliable small-scale experimental platform incorporating various influences generating anomalous temperature data. We employed deep learning autoencoders (AEs) integrate spatiotemporal data, aiming minimize the impact of conditions on performance. This research focused analyzing how changes rapid fluctuations affected capabilities, evaluating metrics such as accuracy delay. Results showed that, compared AE VAE handling spatial or temporal CNN-AE demonstrated superior anomaly performance strong robustness applied Furthermore, findings emphasize that factors extreme temperatures can affect outcomes, increasing likelihood alarms. underscores potential utilizing FO-DTS data with scenarios suggestions mitigating interference practical applications.

Язык: Английский

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Advancements in Artificial Intelligence Applications for Forest Fire Prediction

Forests, Год журнала: 2025, Номер 16(4), С. 704 - 704

Опубликована: Апрель 19, 2025

In recent years, the increasingly significant impacts of climate change and human activities on environment have led to more frequent occurrences extreme events such as forest fires. The recurrent wildfires pose severe threats ecological environments life safety. Consequently, fire prediction has become a current research hotspot, where accurate forecasting technologies are crucial for reducing economic losses, improving management efficiency, ensuring personnel safety property security. To enhance comprehensive understanding wildfire research, this paper systematically reviews studies since 2015, focusing two key aspects: datasets with related tools algorithms. We categorized literature into three categories: statistical analysis physical models, traditional machine learning methods, deep approaches. Additionally, review summarizes data types open-source used in selected literature. further outlines challenges future directions, including exploring risk multimodal learning, investigating self-supervised model interpretability developing explainable integrating physics-informed models constructing digital twin technology real-time simulation scenario analysis. This study aims provide valuable support natural resource enhanced environmental protection through application remote sensing artificial intelligence

Язык: Английский

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A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia

Technology Knowledge and Learning, Год журнала: 2025, Номер unknown

Опубликована: Апрель 2, 2025

Язык: Английский

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A2C: A modular multi-stage collaborative decision framework for human-AI teams

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127318 - 127318

Опубликована: Апрель 1, 2025

Язык: Английский

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