Detection of forest fire using Dezert-Smarandache theory in wireless sensor networks

Abstract

The most common hazard in forest is forest fire. Forest fires are as ancient as the forests themselves which destroy the forests, and can be a great threat to people who live in forests as well as wildlife. They pose a peril not only to the forest wealth but also to the entire regime utterly distressing the bio diversity, the ecology and the environment of a region. The present methods of detection of forest fire using satellite are widely considered to be scarce to foreknow the fires in the forest. Moreover, the satellite based methods of forest fire detection predict the forest fire only after the fire blowout uncontrollable and this method is considered to be futile to forecast the forest fire. Hence, a smart system is introduced which comprises of multiple classifiers to classify the forest fire attributes and fusion methods using Dezert-Smarandache theory, are considered to combine the data and to forecast the fire more accurately and effectively. The experimental results demonstrate the combined approach, which yields better accuracy in envisaging the forest fire.