Predictive Analytics for Early Electrical Fire Detection in IoT Networks Using Random Forest

Ensuring safety against potential fire threats is of utmost significance due to the development of IoT devices in numerous areas. Most of the time, human involvement is required for traditional fire detection systems, which lack proactive capabilities. This research presents a new technique for electrical fire detection in Internet of Things (IoT) networks using predictive analytics, utilizing the Random Forest algorithm. To proactively detect and reduce fire hazards before they become catastrophic by using the abundance of data produced by IoT sensors. Data such as current measurements, temperature, humidity, and other relevant features may be retrieved in real-time via IoT devices that are integrated into electrical systems according to the suggested system. Random Forest classifier is taught to interpret and classified patterns that suggest potential fire dangers using data preparation, feature engineering, and supervised learning methods. Standard validation measures are used to verify the model's performance, providing its dependability and robustness. IoT network infrastructure deployment of the trained model allows for early identification of possible fire hazards and continuous monitoring. The system's capabilities can be including more sensors and advanced machine learning algorithms. By automating the monitoring process and enabling early detection, the suggested solution increases efficiency and safer and more robust IoT environment.