Real-Time Fire Detection Algorithm Based on Support Vector Machine with Dynamic Time Warping Kernel Function

Baek, Jae-Seung; Alhindi, Taha J.; Jeong, Young Seon; Jeong, Myong K.; Seo, Seongho; Kang, Jahyo; Choi, Junyoung; Chung, Hyunsang

doi:10.1007/s10694-020-01062-1

Cited by 18 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Finally, 18% of the reviewed articles use Artificial Intelligence to assist with plotting fire strategy plans, resolving management issues, and monitoring of installations. The use of Support Vector Machines and Dynamic Time Warping Kernel function has been explored with the aim of improving and optimising the performance of fire detection sensors [39]. The capabilities of the Internet of Things (IoT) have been combined with Deep Learning (DL) algorithms to predict developed fire temperatures in compartments [40].…”

Section: Monitoring and Management Planningmentioning

confidence: 99%

A review of the contributions of Artificial Intelligence in fire engineering, in a world rapidly realising the need for sustainable design

Bakas

Kontoleon

2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

With sustainability being at the forefront of international civil and structural engineering professional bodies agendas, Artificial Intelligence (AI), and its numerous facets, has potential of providing an invaluable tool. Although the notion of AI is not new, it only recently started gaining momentum in the sector of fire engineering. The combination of tangible benefits in scientific and industrial applications, in conjunction with a developing understanding of its functionality, requirements and deliverables, from practitioners and researchers alike, supported a wider acceptance of the method. Acknowledging the scope of AI applications is vast, a focus will be given to fire engineering scientific projects. A selection of recent, representative scientific papers will be reviewed with the aim of debating the attributes of Artificial Intelligence in the context of sustainability, while also assessing its current contribution to the technical advancement of the field.

show abstract

Section: Monitoring and Management Planningmentioning

confidence: 99%

A review of the contributions of Artificial Intelligence in fire engineering, in a world rapidly realising the need for sustainable design

Bakas

Kontoleon

2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Support vector machine (SVM) is employed for real-time early fire detection and false alarm reduction [20], along with fire detection using multi-channel sensors [21]. Convolutional neural networks (CNN) are applied in resource-limited fire detection systems [22] and for early detection in video surveillance [23]. Machine learning (ML) techniques are used for bushfire susceptibility mapping in Turkey [24], while deep neural networks are employed for bushfire risk prediction in the Northern Beaches area of Sydney [25] and for data-driven risk mapping in China [26], among other applications.…”

Section: Introductionmentioning

confidence: 99%

Water Reservoir Placement Methodology for Forest Firefighting: A Case Study of Valparaíso, Chile

Alfaro,

Santander,

Fuertes

et al. 2024

Forests

View full text Add to dashboard Cite

Climate change has a significant impact on generating forest fires. These fires damage property, interrupt productive processes, reduce employment sources, and generate direct economic losses. Also, fires contribute to climate change, resulting in a negative cycle. Therefore, the effective management of forest fires is of vital importance. This research focuses on the combat and mitigation phase of forest fires, with special emphasis on using helicopters to transport water from nearby reservoirs to the fire site. The location of these reservoirs is key since a greater distance traveled by helicopter means a longer delay in water transport, which favors the spread of the fire. For this reason, this research proposes an optimization model to determine the optimal location of these reservoirs in a territory. The proposed model is illustrated with a case study of the region of Valparaiso, demonstrating its usefulness for management and decision making when locating reservoirs for firefighting.

show abstract

“…For example, ground-based sensors can complement satellite sensing technology when cloud cover and smoke may interfere with defining the position and velocity of the fire *Correspondence should be addressed to: Peter L. Woodfield, E-mail: p.woodfield@griffith.edu.au Fire Technology Ó 2022 The Author(s) Manufactured in The United States https://doi.org/10.1007/s10694-022-01224-3 front. Multiple data sources can be integrated through machine learning algorithms [12,13].…”

Section: Introductionmentioning

confidence: 99%

Underground LoRa Sensor Node for Bushfire Monitoring

Herring

Sharp²,

Roberts

et al. 2022

Fire Technol

View full text Add to dashboard Cite

The feasibility of an underground LoRaWAN bushfire temperature sensing node from the point of view of survivability in a bushfire event is considered. Thermal penetration into the soil is modelled using a one-dimensional analytical formulation for a semi-infinite solid. A working prototype of the sensor unit was tested experimentally beneath a relatively small (400 mm × 400 mm base) timber fire. A buried LoRa radio (depth 100 mm) with a k-type thermocouple monitored the temperature from beneath the fire. The analysis demonstrates that under dry soil conditions, the time that the sensor node under the fire base can endure is proportional to the square of the depth of burial of the electronic components and inversely proportional to the thermal diffusivity of the soil. The original contribution of the work is in the practical demonstration of the durability of a LoRa sensing node beneath a fire front for bushfire sensing applications.

show abstract

Real-Time Fire Detection Algorithm Based on Support Vector Machine with Dynamic Time Warping Kernel Function

Cited by 18 publications

References 38 publications

A review of the contributions of Artificial Intelligence in fire engineering, in a world rapidly realising the need for sustainable design

A review of the contributions of Artificial Intelligence in fire engineering, in a world rapidly realising the need for sustainable design

Water Reservoir Placement Methodology for Forest Firefighting: A Case Study of Valparaíso, Chile

Underground LoRa Sensor Node for Bushfire Monitoring

Contact Info

Product

Resources

About