Forest Fire Detection System Using IoT and Artificial Neural Network

Forest fire (wildfire) is one of the common hazards that is accrued in the forest. Fire monitoring has three phases: pre-fire (take appropriate action for fire control), during fire (detection of fire and planning to control fire), post-fire (damage assessment and mitigation planning). In older days, manually fire detection approach is used. In current days, satellite-based surveillance system is used to detect forest fire but this works when fire is spread in the large area. So these techniques are not efficient. According to a survey, approximately 80% losses are accrued in the forest due to the late detection of fire. So to overcome this problem, we use the Internet of things technology. In this paper, early fire detection model has been proposed with the help of the Raspberry Pi microcontroller and required sensors. Centralized server is used for storing the data and analyzing that data. Feed-forward fully connected neural network is used for prediction purpose. Then, an alert message is sent to the admin and to the people within the proximity.