Detection of forest fires using machine learning technique: A perspective

Kansal, Aditi; Singh, Yashwant; Kumar, Nagesh; Mohindru, Vandana

doi:10.1109/iciip.2015.7414773

Cited by 44 publications

(8 citation statements)

References 9 publications

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“…However, it is not simple to incorporate machine learning techniques in fire detection systems. Several complications may arise when using ML techniques such as lack of power, data processing requirements, limited communication range and computations; the main issue is faced during the application and specifically the distribution of ML algorithm on sensor nodes [3,18]. (Khamukhin et al, 2017).The authors in Ref.…”

Section: Related Workmentioning

confidence: 99%

FDPA internet of things system for forest fire detection, prediction and behaviour analysis

Alkhatib,

Jaber

2024

IET Wireless Sensor Systems

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The escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the authors introduce the Forest Fire Detection, Prediction, and Behaviour Analysis (FDPA) system, an innovative Internet of Things (IoT) solution. The FDPA system leverages a wireless sensor network to efficiently detect and analyse fire behaviour, providing real‐time data on fire spread, speed, and direction. Uniquely, it can anticipate natural fires hours before they occur by monitoring ecological parameters such as humidity, temperature, and using the Chandler Burning Index (CBI) for quantifying fire danger. Designed for the challenging forest environment, the FDPA system prioritises minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. Field tests of the FDPA system in various Jordanian forest locations, including Burgish–Ajloun, have demonstrated its effectiveness. The trials revealed the system's capability in early fire detection, low latency response, predicting fire behaviour, and determining fire spread direction. Continuous monitoring of the forest ecosystem and rapid detection allow authorities to act swiftly, preventing potential fires from escalating. Furthermore, the system tracks ecological changes within the forest, offering insights into imminent natural fires. This feature enables proactive measures to mitigate fire spread, safeguarding the environment and nearby communities. The strategic placement of sensor nodes and the use of durable yet straightforward components reduce the risk of system damage due to environmental extremities. Overall, the FDPA system emerges as a promising tool for forest fire management. Its ability to detect, predict, and analyse forest fires in real‐time positions it as a vital asset in minimising the detrimental impacts of forest fires on the environment and human settlements.

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Section: Related Workmentioning

confidence: 99%

FDPA internet of things system for forest fire detection, prediction and behaviour analysis

Alkhatib,

Jaber

2024

IET Wireless Sensor Systems

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“…The others research have been done is application of WSN in predicting natural disasters like hailstorm, fire, rainfall etc. by WSN are infrequent and stochastic [7]. As well as in design and implementation of a smart fire detection system using a WSN and Global System for Mobile (GSM) communication to detect fires effectively and reduce false positives, the system uses smoke and temperature sensors [8,9].…”

Section: [2] Related Workmentioning

confidence: 99%

Iot Integrated Forest Fire Detection and Prediction Using Node Mcu

Anon¹,

Ranjith²,

Varalakshmi³

2021

IJEEA

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Forest fires in Riau Province Indonesia are issues that regularly happen with affected the length and breadth of Indonesia. The effects forest fire and of haze on human health as reported in that particular year were about 20 million people have suffered from respiratory problems and serious deterioration in overall health in Riau community. This research proposes development of Wireless Sensor Network (WSN) for detection of forest fire in Riau province for the region that high risk forest fire in dry session, with WSN technology data be able to collect from the sensor deploy in the forest area. The deployment of sensors will be located at several locations that has badly impacted in previous case and forecast location with potential fire happen. Mathematical analysis is used in this case for modelling number of sensor required to deploy and the size of forest area represented overall of Riau Province. An early indication of forest fires is needed for quick prevention before they become uncontrollable and overwhelming. The design and development of WSN sensors give high feasibility to overcome current issues in Riau Province because of forest fire. The development of this system used WSN highly applicable for early warning and alert to representative institution for action taken.

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“…The WSN Simulator address important design issues as: coverage of the area under surveillance in relation to initial sensor deployment, number of sensors needed for targeted deployment, and coverage change as function of time. A new approach for forest fire monitoring and detection as discussed in [15,16] which using data aggregation in WSN. The proposed approach can provide faster and efficiently reaction to forest fires while consuming economically WSN's energy, which has been validated and evaluated in extensive simulation experiments.…”

Section: Related Workmentioning

confidence: 99%

“…The others research has been done is application of WSN in predicting natural disasters like hailstorm, fire, rainfall etc. by WSN are infrequent and and stochastic [16]. As well as in design and implementation of a smart fire detection system using a WSN and Global System for Mobile (GSM) communication to detect fires effectively and reduce false positives, the system uses smoke and temperature sensors [22].…”

Section: Related Workmentioning

confidence: 99%

Modelling of wireless sensor networks for detection land and forest fire hotspot

et al. 2019

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Indonesia located in South East Asia countries with tropical region, forest fires in Indonesia is one of big issue and disaster because it happens in almost of every year, this is because of some of region consist of peat land that high risk for fire especially in dry season. Riau Province is one of region that regularly incident of forest fire with affected the length and breadth of Indonesia. Propose development of Wireless Sensor Networks (WSNs) for detection of land and forest fire hotspot in Indonesia as well as one of the main consents in this research, case location in Riau province is at one of the regions that high risk forest fire in dry season. WSNs technology used for ground sensor system to collect environmental data. Data training for fire hotspot detection is done in data center to determine and conclude of fire hotspot then potential to become big fire. The deployment of sensors located at several locations that has potential for fire incident, especially as data shown in previous case and forecast location with potential fire happen. Mathematical analysis is used in this case for modelling number of sensors required to deploy and the size of forest area. The design and development of WSNs give high impact and feasibility to overcome current issues of forest fire and fire hotspot detection in Indonesia. The development of this system used WSNs highly applicable for early warning and alert system for fire hotspot detection.

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Detection of forest fires using machine learning technique: A perspective

Cited by 44 publications

References 9 publications

FDPA internet of things system for forest fire detection, prediction and behaviour analysis

FDPA internet of things system for forest fire detection, prediction and behaviour analysis

Iot Integrated Forest Fire Detection and Prediction Using Node Mcu

Modelling of wireless sensor networks for detection land and forest fire hotspot

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