Real-Time Wildfire Monitoring and Alert System Using GSM Technology

N, Aniedu A.; C.I, Chukwuneke; C, Asogwa D.; Nwokoye, C S

doi:10.9790/0050-03040110

Cited by 3 publications

(1 citation statement)

References 2 publications

(2 reference statements)

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“…However, the sensitivity of the system was still low because it cannot distinguish between the real and non-real fire smoke. MQ-2 type gas sensor was used in [6] to detect smoke in the developed system. However, there was no work to optimize the ability of the system to detect smoke which causes the sensitivity of the system was low.…”

Section: -Illustration Of the Smoke From The Car Motorcycle And The F...mentioning

confidence: 99%

High Sensitivity Prototype Kit with IoT System for Fire Detection in a Peatland Swamp Forest Area

Hamzah,

Dalimin,

Md Som

et al. 2023

IJIE

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Smoke sensor of the photoelectric type is often used in indoor fire detection system. It is very sensitive to fire smoke and will trigger a fire alarm quickly when the presence of smoke is detected.By making a small innovation, this sensor can be implemented outdoors by means of IoT as used in the forest fire early detection system. However, the typical use of the sensor without any modification causes the sensitivity of the system to be very low such as failing to identify the actual presence of smoke. To increase the sensitivity of systems that use this type of sensor, the developed system is equipped withan automatic reclosing operation that is able to distinguish real smoke or not. The system has been designed to trigger cloud servers only after detecting smoke for ten seconds or beyond. System equipped with automatic reclosing operation show better response by not triggering cloud servers when detecting temporary smoke from car, motorcycle or nearby factory. When comparing the system before and after using this operation is, it was found that the system no longer sends smoke notification message to the user via Telegram channel when this temporary smoke is detected

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Section: -Illustration Of the Smoke From The Car Motorcycle And The F...mentioning

confidence: 99%

High Sensitivity Prototype Kit with IoT System for Fire Detection in a Peatland Swamp Forest Area

Hamzah,

Dalimin,

Md Som

et al. 2023

IJIE

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Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

Lever

Arcucci

2022

J Comput Soc Sc

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The intensity of wildfires and wildfire season length is increasing due to climate change, causing a greater threat to the local population. Much of this population are increasingly adopting social media, and sites like Twitter are increasingly being used as a real-time human-sensor network during natural disasters; detecting, tracking and documenting events. The human-sensor concept is currently largely omitted by wildfire models, representing a potential loss of information. By including Twitter data as a source in our models, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This paper implements machine learning in a wildfire prediction model, using social media and geophysical data sources with Sentiment Analysis to predict wildfire characteristics with high accuracy. We also use wildfire-specific attributes to predict online social dynamics, as this has been shown to be indicative of localised disaster severity. This may be useful for disaster management teams in identifying areas of immediate danger. We combine geophysical satellite data from the Global Fire Atlas with social data provided by Twitter. We perform data collection and subsequent analysis & visualisation, and compare regional differences in online social sentiment expression. Following this, we compare and contrast different machine learning models for predicting wildfire attributes. We demonstrate social media is a predictor of wildfire activity, and present models which accurately model wildfire attributes. This work develops the concept of the human sensor in the context of wildfires, using users’ Tweets as noisy subjective sentimental accounts of current localised conditions. This work contributes to the development of more socially conscious wildfire models, by incorporating social media data into wildfire prediction and modelling.

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LPWAN Based IoT Surveillance System for Outdoor Fire Detection

Roque

Padilla

2020

IEEE Access

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Many fire situations have represented the loss of lives and material costs due to the lack of early fire detection through smoke or gas sensing, which can become complex and critical. Meanwhile, engineers worldwide develop and test multiple systems for smoke and gas detection, commonly based on sensor networks, digital image processing, or computer vision. Furthermore, the detection system must work thoroughly with alarms and warnings that aware of a risk situation for prompt evacuation of the population in the surroundings based on a reliable data network topology with adequate device deployments that will let us know the moment a fire outbreak. This paper presents a low-cost Internet of Things (IoT) prototype for fire detection in outdoor environments based on sensors and Low Power Wide Area Network (LPWAN), focused on the accuracy in the temperature and gas measurement at the moment a fire starts. For its achievement, we integrated wireless components, development boards, and electronic devices, following the management of information updates through a database schema for the alarm settings based on the data gathered from the sensors. INDEX TERMS database systems, emergency services, gas detectors, sensor systems, temperature measurement.

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Real-Time Wildfire Monitoring and Alert System Using GSM Technology

Cited by 3 publications

References 2 publications

High Sensitivity Prototype Kit with IoT System for Fire Detection in a Peatland Swamp Forest Area

High Sensitivity Prototype Kit with IoT System for Fire Detection in a Peatland Swamp Forest Area

Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

LPWAN Based IoT Surveillance System for Outdoor Fire Detection

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