Forest Fire Detection and Temperature Monitoring Alert using IoT and Machine Learning Algorithm

Abstract

Forest fires are a prevalent hazard in forests that significantly damage wildlife and the environment. It may be averted if a comprehensive system is installed in forest regions to detect fires and inform firefighting authorities to take timely action. The goal of this project is to create an Internet of Things (IoT)-based real-time detection system that detects fires and sends emergency notices to authorities. A GSM/GPRS module interacts with an IoT server because network bandwidth is typically very poor or nonexistent in forest regions. As a result, a 2G network is ideal for communicating with the server. A real-time fire monitoring system that differentiates fire and smoke is used to identify an actual fire occurrence. The nano-based Atmega328 IoT gateway identifies the forest’s fire as soon as possible and acts rapidly before it spreads across a large area. Here, this uses machine learning algorithms to detect the event of a fire in a large region. The system uses a flame sensor to detect the flame and a temperature sensor to measure the temperature in a particular area. By using the machine learning method, the system can easily give the result with actual detection and intimate the authority about the temperature condition. The data are then sent to the cloud-based application. In the event of an unusual rise in forest temperature, this will alert the forest authorities and sound the fire alarm. It can also predict future fires by using machine learning. This is accomplished using the fog computing method. Due of the sensors’ efficiency, this might potentially be applied in industrial settings. Any type of forest can make use of it. From this experiment, this research study deduced that it has a remarkable accuracy of 98% in predicting forest fires. As a result, the possibility of a false alarm is significantly decreased.