Advancements in Artificial Intelligence Applications for Forest Fire Prediction DOI Open Access
Hui Liu,

Lifu Shu,

Xiaodong Liu

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 704 - 704

Published: April 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

Language: Английский

Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire Monitoring DOI Creative Commons
Haitao Bian,

Xiaohan Luo,

Zhichao Zhu

et al.

Fire, Journal Year: 2025, Volume and Issue: 8(1), P. 23 - 23

Published: Jan. 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.

Language: Английский

Citations

0
A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia DOI Creative Commons
Harikesh Singh, Li-Minn Ang,

Dipak Paudyal

et al.

Technology Knowledge and Learning, Journal Year: 2025, Volume and Issue: unknown

Published: April 2, 2025

Language: Английский

Citations

0
A2C: A modular multi-stage collaborative decision framework for human-AI teams DOI
Shahroz Tariq, Mohan Baruwal Chhetri, ‪Surya Nepal‬

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127318 - 127318

Published: April 1, 2025

Language: Английский

Citations

0
Advancements in Artificial Intelligence Applications for Forest Fire Prediction DOI Open Access
Hui Liu,

Lifu Shu,

Xiaodong Liu

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 704 - 704

Published: April 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

Language: Английский

Citations

0