scholarly journals Forest Fire Detection and Prediction for Alert Generation and Safety Measure

2021 ◽  
Vol 9 (VI) ◽  
pp. 4510-4515
Author(s):  
Satya Sai Saalini Lanka
Keyword(s):  
Early Detection ◽  
Forest Fire ◽  
Physical Quantity ◽  
Single Unit ◽  
Reporting System ◽  
Fire Detection ◽  
Large Area ◽  
Distant Location ◽  
Self Powered ◽  
Forest Fire Detection

Forest is an important asset of this world as it creates ecological balance and provides several resources that are helpful to mankind. It is important to preserve the forest areas. Fire is one of the most dangerous threats to the forest. Every year number of forest areas reported to catch fire. Many areas in forest are still not on surveillance to provide information of fire. Thus it spreads over large area and destroys forest. Amazon fire is recently occurred fire. In this project a prototype is designed such that it will detect and inform about the occurrence of fire at distant location. The system proposed in this project comprises of two sensors, namely smoke and fire. These sensors detect change in small measurable physical quantity and help within the early detection of a fire. Large number of self powered monitoring units are placed over the forest area. In this project a single unit with reporting system is presented and evaluated.

Study of routing protocols used in sensor networks for forest fire detection

2020 ◽  
Author(s):  
Jose Guaman'Quiche ◽  
Edwin Guaman-Quinche ◽  
Hernan Torres-Carrion ◽  
Wilman Chamba-Zaragocin ◽  
Franciso Alvarez-Pineda
Keyword(s):  
Sensor Networks ◽  
Forest Fire ◽  
Routing Protocols ◽  
Fire Detection ◽  
Forest Fire Detection

A novel approach of WSN routing protocols comparison for forest fire detection

2018 ◽  
Vol 26 (3) ◽  
pp. 1857-1867 ◽  
Author(s):  
Noureddine Moussa ◽  
Abdelbaki El Belrhiti El Alaoui ◽  
Claude Chaudet
Keyword(s):  
Forest Fire ◽  
Routing Protocols ◽  
Fire Detection ◽  
Novel Approach ◽  
Forest Fire Detection

scholarly journals Forest Fire Detection via Feature Entropy Guided Neural Network

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 128
Author(s):  
Zhenwei Guan ◽  
Feng Min ◽  
Wei He ◽  
Wenhua Fang ◽  
Tao Lu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Forest Fire ◽  
State Of The Art ◽  
Fire Detection ◽  
High Entropy ◽  
Training Samples ◽  
Feature Information ◽  
Forest Fire Detection ◽  
Entropy Source

Forest fire detection from videos or images is vital to forest firefighting. Most deep learning based approaches rely on converging image loss, which ignores the content from different fire scenes. In fact, complex content of images always has higher entropy. From this perspective, we propose a novel feature entropy guided neural network for forest fire detection, which is used to balance the content complexity of different training samples. Specifically, a larger weight is given to the feature of the sample with a high entropy source when calculating the classification loss. In addition, we also propose a color attention neural network, which mainly consists of several repeated multiple-blocks of color-attention modules (MCM). Each MCM module can extract the color feature information of fire adequately. The experimental results show that the performance of our proposed method outperforms the state-of-the-art methods.

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