Forest fire prediction using IoT and deep learning

Forests are the most important part of the human life as it maintains an environmental balance to get proper rain and sufficient resources accordingly. The major threat raising in forest areas is a fire, in which the forest fire scenario is the most important cause to destroy many trees and animals within a few hours. The technologies such as deep learning, IoT and smart sensors provide a lead to design a smart forest fire prediction scheme to support nature to manage the ecosystem in the proper way. This paper is intended to design a forest fire prediction mechanism. Learning-based forest fire prediction scheme (LBFFPS) based on deep learning has been proposed for the prediction in the timely manner. This approach identifies the forest fire based on the sensor unit associated with the system with respect to the learning logics. A digital camera with 1020-megapixel has adapted for the surveillance. The sensor unit consists of two different and powerful sensors such as smoke identification sensor and the temperature and humidity monitoring sensor. Based on these two sensors the surrounding smoke presence, temperature and the humidity level have been identified and reported using the NodeMCU controller. In this application, internet of things (IoT) is associated, to provide a wireless communication alert ability. It collects and maintain the information regarding the forest provided by the sensor unit to the remote cloud server environment. The NodeMCU microcontroller has an inbuilt WiFi to acquire the internet signals and provides a constant bridge between the sensor unit and the server end for remote data maintenance. The proposed logic is helpful to identify the fire signals and inform the respective person to take appropriate action to prevent the forest fire.