scholarly journals Forest fire detection system using wireless sensor networks and machine learning

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Udaya Dampage ◽  
Lumini Bandaranayake ◽  
Ridma Wanasinghe ◽  
Kishanga Kottahachchi ◽  
Bathiya Jayasanka
Keyword(s):  
Machine Learning ◽  
Power Supply ◽  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Weather Conditions ◽  
Rechargeable Batteries ◽  
Wireless Sensor ◽  
Forest Sites ◽  
Initial Stage

AbstractForest fires have become a major threat around the world, causing many negative impacts on human habitats and forest ecosystems. Climatic changes and the greenhouse effect are some of the consequences of such destruction. Interestingly, a higher percentage of forest fires occur due to human activities. Therefore, to minimize the destruction caused by forest fires, there is a need to detect forest fires at their initial stage. This paper proposes a system and methodology that can be used to detect forest fires at the initial stage using a wireless sensor network. Furthermore, to acquire more accurate fire detection, a machine learning regression model is proposed. Because of the primary power supply provided by rechargeable batteries with a secondary solar power supply, a solution is readily implementable as a standalone system for prolonged periods. Moreover, in-depth attention is given to sensor node design and node placement requirements in harsh forest environments and to minimize the damage and harmful effects caused by wild animals, weather conditions, etc. to the system. Numerous trials conducted in real tropical forest sites found that the proposed system is effective in alerting forest fires with lower latency than the existing systems.

scholarly journals Forest Fire Detection System using Wireless Sensor Networks and Machine Learning

2021 ◽  
Author(s):  
Udaya Dampage ◽  
D. M. L. N. Bandaranayake ◽  
W. M.R. S. Wanasinghe ◽  
K. O. Kottahachchi ◽  
W. A. B. Jayasanka
Keyword(s):  
Machine Learning ◽  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Weather Conditions ◽  
Wireless Sensor ◽  
Human Beings ◽  
Forest Environment ◽  
Initial Stage ◽  
Negative Impacts

Abstract Forest fires have become a major threat around the world, causing many negative impacts on human beings and forest ecosystems. Even though rapid climatic changes arising from high environmental pollution, greenhouse effects, etc. have caused this situation, a higher percentage of forest fires occur due to human activities. Therefore, to minimize the destruction caused by forest fires, the need to detect forest fires at their initial stage is needed. This paper proposes a model that can be used to detect forest fires at the initial stage using a wireless sensor network. Furthermore, to acquire more accurate fire detection, a machine learning regression model is proposed. Moreover, thorough attention is given to sensor node design and node placement in the forest to be fitted in the forest environment to minimize the damage and harmful effects caused by wild animals, weather conditions, etc. to the system.

scholarly journals Fire detection and notification using a Zigbee wireless sensor network

2021 ◽  
Vol I (I) ◽  
Author(s):  
Priyadharshini S
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Forest Fire ◽  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Wireless Sensor ◽  
And Control ◽  
Forest Conditions

Forest fires are the most common threat in the woods. A combination of natural and human-made factors contributes to forest fires. Forest fires destroy trees, which are essential to produce oxygen, which we need to live. This new Zigbee-based wireless sensor network is being developed to overcome the limitations of existing technologies like the MODIS satellite-based detection system and a basic wireless sensor network. It's difficult to contain a forest fire that wasn't predicted or noticed in time. As a result, it's critical to catch a wildfire early enough before it spreads too far. Using a GSM device, the proposed method would gather data on forest conditions such as temperature, humidity, smoke, and flames, and deliver it to the appropriate authorities. There are three parts to the project's concept. Modules for sensors, gateways, and control centres make up the three sections. This project's main objective is to benefit others.

scholarly journals Fire Detection System Using Machine Learning

2021 ◽  
Vol 1916 (1) ◽  
pp. 012209
Author(s):  
A Arul ◽  
R S Hari Prakaash ◽  
R Gokul Raja ◽  
V Nandhalal ◽  
N Sathish Kumar
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Fire Detection ◽  
Fire Detection System

Modelling the effects of distance on the probability of fire detection from lookouts

2006 ◽  
Vol 15 (2) ◽  
pp. 197 ◽  
Author(s):  
Francisco Castro Rego ◽  
Filipe Xavier Catry
Keyword(s):  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Fast Response ◽  
Fire Loss ◽  
Detection Function ◽  
Related Factors ◽  
New Approach ◽  
Forest Fire Management ◽  
In Fire

In the management of forest fires, early detection and fast response are known to be the two major actions that limit both fire loss and fire-associated costs. There are several inter-related factors that are crucial in producing an efficient fire detection system: the strategic placement and networking of lookout towers, the knowledge of the fire detection radius for lookout observers at a given location and the ability to produce visibility maps. This study proposes a new methodology in the field of forest fire management, using the widely accepted Fire Detection Function Model to evaluate the effect of distance and other variables on the probability that an object is detected by an observer. In spite of the known variability, the model seems robust when applied to a wide variety of situations, and the results obtained for the effective detection radius (13.4 km for poor conditions and 20.6 km for good conditions) are in general agreement with those proposed by other authors. We encourage the application of the new approach in the evaluation or planning of lookout networks, in addition to other integrated systems used in fire detection.

scholarly journals Early Fire Detection System in Tanzania Markets

2020 ◽  
Vol 2 (1) ◽  
pp. 50
Author(s):  
Rambo Hilary ◽  
Philemon Rotich ◽  
Anna Geofrey ◽  
Anael Sam
Keyword(s):  
Data Processing ◽  
Mobile Application ◽  
Detection System ◽  
Early Stage ◽  
Fire Detection ◽  
Wireless Sensor ◽  
Multiple Sensors ◽  
Fire Detection System ◽  
Processing Subsystem ◽  
Fire Outbreak

Application of wireless sensor networks (WSN) and Internet of Things (IoT) used to provide real-time monitoring of fire outbreak in markets. The system integrates three subsystems namely; sensing subsystem which uses multiple sensors for detecting fire outbreaks. Data processing subsystem which collects data from the sensing subsystem through Xbee, analyses, and uploads data to the cloud. If values exceed the sensor threshold, an alarm is triggered and notification is sent to stakeholders via mobile application subsystem. The integration between sensing, data processing, and mobile application subsystems pave a new way for the mitigation of fire outbreaks at its early stage.

scholarly journals Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2025 ◽  
Author(s):  
Jun Hong Park ◽  
Seunggi Lee ◽  
Seongjin Yun ◽  
Hanjin Kim ◽  
Won-Tae Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Transfer ◽  
Detection System ◽  
Smart Cities ◽  
Fire Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Fire Detection System

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

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