Forest Fire Monitoring using Internet of Things and Machine Learning

Ecosystems and human livelihoods are seriously threatened by forest fires and wood theft. To reduce these hazards, prompt notice and action are essential. The challenges faced in the current forest fire monitoring systems are that the fire detection proportions are low, lack of real time operation, and high false indication. Using cutting-edge technology, a comprehensive forest fire and theft alert system is designed in this context. This system will monitor and report in real-time forest fires and illicit logging activity by integrating vibration and flame sensors with an Arduino-based GPS module. Sensor data is sent to a cloud-based Internet of Things (IoT) platform for instantaneous warning and analysis. The design, development, and implementation of this cutting-edge technology are described in the proposed system, with an emphasis on its potential for data-driven forest management, early detection, and quick response. Also, machine learning algorithms based on K Nearest Neighbour (KNN) and Random forest classifiers are used to predict forest fires.