Predicting the Duration of Forest Fires Using Machine Learning Methods DOI Creative Commons
Constantina Kopitsa, Ioannis G. Tsoulos,

Vasileios Charilogis

и другие.

Future Internet, Год журнала: 2024, Номер 16(11), С. 396 - 396

Опубликована: Окт. 28, 2024

For thousands of years forest fires played the role a regulator in ecosystem. Forest contributed to ecological balance by destroying old and diseased plant material; but modern era are major problem that tests endurance not only government agencies around world, also have an effect on climate change. become more intense, destructive, deadly; these known as megafires. They can cause economic problems, especially summer months (dry season). However, humanity has developed tool predict fire events, detect them time, their duration. This is artificial intelligence, specifically, machine learning, which one part AI. Consequently, this paper briefly mentions several methods learning used predicting early detection, submitting overall review current models. Our main objective venture into new field: duration ongoing fires. contribution offers way manage fires, using accessible open data, available from Hellenic Fire Service. In particular, we imported over 72,000 data 10-year period (2014–2023) techniques. The experimental validation results than encouraging, with Random achieving lowest value for error range (8–13%), meaning it was 87–92% accurate prediction Finally, some future directions extend research presented.

Язык: Английский

Identification method of forest fire risk factors and their coupling relationship driven by attribute dependence DOI
Enhui Zhao, Ning Wang, Song Cui

и другие.

International Journal of Disaster Risk Reduction, Год журнала: 2025, Номер unknown, С. 105529 - 105529

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Mapping wildfire susceptibility in the tropical region of Brunei: a machine learning and explainable AI approach using google earth engine with remote sensing data DOI
Rufai Yusuf Zakari, Owais Ahmed Malik,

Ong Wee-Hong

и другие.

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Май 14, 2025

Язык: Английский

Процитировано

0
A new weighted rough set and improved BP neural network method for predicting forest fires DOI
Enhui Zhao, Ning Wang, Song Cui

и другие.

Reliability Engineering & System Safety, Год журнала: 2025, Номер unknown, С. 111206 - 111206

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Sustainable Risk Management Framework for Petroleum Storage Facilities: Integrating Bow-Tie Analysis and Dynamic Bayesian Networks DOI Open Access
Dingding Yang, Kexin Xing,

Lidong Pan

и другие.

Sustainability, Год журнала: 2025, Номер 17(6), С. 2642 - 2642

Опубликована: Март 17, 2025

Petroleum storage and transport systems necessitate robust safety measures to mitigate oil spill risks threatening marine ecosystems sustainable development through ecological socioeconomic safeguards. We aimed gain a deeper understanding of the evolution patterns accidents effectively risks. An improved risk assessment method that combines Bow-Tie (BT) theory Dynamic Bayesian was applied evaluate petroleum transportation facilities. Additionally, scenario modeling approach utilized construct model event chain resulting from accidents, facilitating quantitative analysis prediction. By constructing an accident based on fault trees, BT converted into Network (BN) model. A (DBN) established by incorporating time series parameters static model, enabling dynamic base in Zhoushan archipelago. This study quantitatively analyzes propagation process tank leakage, establishing time-dependent probability profiles. The results demonstrate initial leakage 0.015, with magnitude doubling for temporal progression concurrent probabilistic escalation secondary hazards, including fire or explosion scenarios. novel transition framework consequences petrochemical leaks has been developed, providing predictive paradigm trajectories offering critical theoretical practical references emergency response optimization.

Язык: Английский

Процитировано

0
Urban flood risk evaluation using social media data and Bayesian network approach: a spatial-temporal dynamic analysis in Wuhan city, China DOI
Yihan Zhang, Jian Fang, Dingtao Shen

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106388 - 106388

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Predicting the Duration of Forest Fires Using Machine Learning Methods DOI Creative Commons
Constantina Kopitsa, Ioannis G. Tsoulos,

Vasileios Charilogis

и другие.

Future Internet, Год журнала: 2024, Номер 16(11), С. 396 - 396

Опубликована: Окт. 28, 2024

For thousands of years forest fires played the role a regulator in ecosystem. Forest contributed to ecological balance by destroying old and diseased plant material; but modern era are major problem that tests endurance not only government agencies around world, also have an effect on climate change. become more intense, destructive, deadly; these known as megafires. They can cause economic problems, especially summer months (dry season). However, humanity has developed tool predict fire events, detect them time, their duration. This is artificial intelligence, specifically, machine learning, which one part AI. Consequently, this paper briefly mentions several methods learning used predicting early detection, submitting overall review current models. Our main objective venture into new field: duration ongoing fires. contribution offers way manage fires, using accessible open data, available from Hellenic Fire Service. In particular, we imported over 72,000 data 10-year period (2014–2023) techniques. The experimental validation results than encouraging, with Random achieving lowest value for error range (8–13%), meaning it was 87–92% accurate prediction Finally, some future directions extend research presented.

Язык: Английский

Процитировано

0