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.

Neural fuzzy network applied in fire detection system

2000 ◽  
Author(s):  
Zhiquan Li ◽  
Chunling Fan ◽  
Lingshi Yao ◽  
Xifu Qiang
Keyword(s):  
Detection System ◽  
Fire Detection ◽  
Fire Detection System ◽  
Fuzzy Network ◽  
In Fire ◽  
Neural Fuzzy Network ◽  
Neural Fuzzy

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 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.

A Forest Fire Detection System

2013 ◽  
pp. 179-190
Author(s):  
Vassileios Tsetsos ◽  
Odysseas Sekkas ◽  
Evagellos Zervas
Keyword(s):  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Decision Fusion ◽  
Visible Range ◽  
Detection Algorithms ◽  
Integrated Technology ◽  
Detection Systems ◽  
Individual Decisions ◽  
Fusion Scheme

Forest fires cause immeasurable damages to indispensable resources for human survival, destroy the balance of earth ecology, and worst of all they frequently cost human lives. In recent years, early fire detection systems have emerged to provide monitoring and prevention of the disasterous forest fires. Among them, the Meleager1 system aims to offer one of the most advanced and integrated technology solutions for fire protection worldwide by integrating several innovative features. This chapter outlines one of the major components of the Meleager system, that is the visual fire detection sybsystem. Groundbased visible range PTZ cameras monitor the area of interest, and a low level decision fusion scheme is used to combine individual decisions of numerous fire detection algorithms. Personalized alerts and induced feedback is used to adapt the detection process and improve the overall system performance.

scholarly journals PENGENDALIAN KEBAKARAN HUTAN DI KPH MADIUN PERUM PERHUTANI UNIT II JAWA TIMUR Forest Fire Management in KPH Madiun Perum Perhutani Unit II East Java

2018 ◽  
Vol 8 (3) ◽  
pp. 183-190
Author(s):  
Bambang Hero Saharjo ◽  
Elga Tiara Putra
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Fire Management ◽  
Qualitative Description ◽  
Local People ◽  
Trigger Factors ◽  
Community Based ◽  
Fire Monitoring ◽  
Forest Fire Management ◽  
In Fire

Forest fires bring substantial losses in many aspects, especially for forest resources. Therefore, forest fire management should take into account at each of Indonesian forest area. KPH Madiun has suffered from a large forest fire in the recent 5 years, thus research to analyze the trigger factors and an effort to manage forest fire should be gone. This research used data triangular methods for data collecting and qualitative description analyse to analyze the data. Results of this research clearly shows that forest fire in KPH Madiun was mainly came from local people activities such as, burning the forest to clear the land (43%) and due to social conflict (15%). The forest fire prepetion emphasized on social approach in community based forest management (CBFM) by planting medicinal plant and establishing the forest Danger Index (FDI) board. The local people participated in fire extinction (49%), while the other not participate yet. There was no fire truck and fire monitoring tower found in the study area. Finding and arresting the suspect behind forest fire is the most difficult thing to do in post-fire management.Key words: forest fire, local people, fire management

A Forest Fire Detection System

2013 ◽  
pp. 1088-1098
Author(s):  
Vassileios Tsetsos ◽  
Odysseas Sekkas ◽  
Evagellos Zervas
Keyword(s):  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Decision Fusion ◽  
Visible Range ◽  
Detection Algorithms ◽  
Integrated Technology ◽  
Detection Systems ◽  
Individual Decisions ◽  
Fusion Scheme

Forest fires cause immeasurable damages to indispensable resources for human survival, destroy the balance of earth ecology, and worst of all they frequently cost human lives. In recent years, early fire detection systems have emerged to provide monitoring and prevention of the disasterous forest fires. Among them, the Meleager1 system aims to offer one of the most advanced and integrated technology solutions for fire protection worldwide by integrating several innovative features. This chapter outlines one of the major components of the Meleager system, that is the visual fire detection sybsystem. Groundbased visible range PTZ cameras monitor the area of interest, and a low level decision fusion scheme is used to combine individual decisions of numerous fire detection algorithms. Personalized alerts and induced feedback is used to adapt the detection process and improve the overall system performance.

scholarly journals A Forest Fire Detection System Based on Ensemble Learning

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 217
Author(s):  
Renjie Xu ◽  
Haifeng Lin ◽  
Kangjie Lu ◽  
Lin Cao ◽  
Yunfei Liu
Keyword(s):  
Ensemble Learning ◽  
Forest Fire ◽  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Ground Truth ◽  
False Positives ◽  
Learning Technology ◽  
Global Information ◽  
Forest Fire Detection

Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the deep learning technology is applied to learn and extract features of forest fires adaptively. However, the limited learning and perception ability of individual learners is not sufficient to make them perform well in complex tasks. Furthermore, learners tend to focus too much on local information, namely ground truth, but ignore global information, which may lead to false positives. In this paper, a novel ensemble learning method is proposed to detect forest fires in different scenarios. Firstly, two individual learners Yolov5 and EfficientDet are integrated to accomplish fire detection process. Secondly, another individual learner EfficientNet is responsible for learning global information to avoid false positives. Finally, detection results are made based on the decisions of three learners. Experiments on our dataset show that the proposed method improves detection performance by 2.5% to 10.9%, and decreases false positives by 51.3%, without any extra latency.

scholarly journals Forest Fire Detection System using Zig Bee

2020 ◽  
Vol 9 (6) ◽  
pp. 15-17
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Disaster Response ◽  
Detection System ◽  
Environmental Parameters ◽  
Fire Detection ◽  
Fire Departments ◽  
Average Temperature ◽  
Response Capacity ◽  
Forest Fire Detection

The forest fires are alone not a threat to the environment but this add on multiple factors which are threat to the environment few examples are decreasing of ground water level, landslides, global warming. These are dangerous for the animal species and few vulnerable species. In recent years few a case has been heard of forest fires in Amazon, California which damaged the flora and fauna. Scientist have also predicted that by end of twentieth century the average temperature of earth will rise by 4.4 degree Celsius. The forest fire detection will give strength to the disaster response capacity. Environmental parameters such as forest temperature and humidity can be tracked in real time. From the information gathered by the system, wildfire fighting or fire prevention decisions can be taken as quickly as possible by the respective fire departments.

scholarly journals Forest Fire Detection System

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1138-1142
Keyword(s):  
Real Time ◽  
Forest Fire ◽  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Wildlife Habitat ◽  
Support Vector ◽  
Time Data ◽  
Temporal Segmentation ◽  
Forest Fire Detection

The forest is one of the most important wealth of every country. The forest fires destroys the wildlife habitat, damages the environment, affects the climate, spoils the biological properties of the soil, etc. So the forest fire detection is a major issue in the present decade. At the same time the forest fire have to be detected as fast as possible. In the proposed method, a color spatial segmentation, temporal segmentation, global motion compensation, Support Vector Machine (SVM) classifications are used to detect the fire and to segment the fire from the video sequence. The method is implemented over the two real time data sets. The proposed method is most suitable for segmenting fire events over unconstrained videos in real time.

An Efficient Rule Based Algorithm for Fire Detection on Real Time Videos

2020 ◽  
Vol 17 (1) ◽  
pp. 308-315
Author(s):  
P. Sridhar ◽  
Latha Parameswaran ◽  
Senthil Kumar Thangavel
Keyword(s):  
Detection System ◽  
Color Space ◽  
Fire Detection ◽  
True Positive Rate ◽  
Color Model ◽  
Detection Accuracy ◽  
Smart Building ◽  
Ycbcr Color Space ◽  
Positive Rate ◽  
In Fire

The projected work shows generic rule in YCbCr color space based fire pixel detection is proposed for smart building which will complement the conventional electronic sensor based fire detection system. The proposed method handles YCbCr color model is used for decoupling the luminance and chrominance which added discriminate the color than RGB color model. This algorithm has been tested on fire and fire like images which results in 97.95% detection accuracy. Obtained experimental results have been compared with other existing algorithms and it is observed that gives a very high the proposed algorithm detection accuracy and feasible true positive rate in fire images.

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