Desarrollo de un sistema loT para la predicción de incendios y la gestión de riesgos en areas industriales urbanas

Abstract

This project proposes an intelligent fire detection system based on the Internet of Things (IoT) and Machine Learning, aimed at preventing and mitigating fires in urban and industrial areas. Using an IoT sensor network, key environmental data such as temperature, humidity, levels of combustible gases, and airborne particles will be collected. This data will be analyzed in real-time using Machine Learning algorithms trained to identify patterns associated with fires, enabling precise and timely detection. The generated alerts will be automatically sent via mobile applications or web platforms, ensuring an immediate response. The combination of IoT and Machine Learning provides an innovative solution that not only detects fires in their early stages but also predicts their occurrence, reducing response times and minimizing damage. This intelligent system significantly contributes to risk management, public safety, and environmental protection, positioning itself as a key advancement in fire prevention for critical environments. Keywords: Noise pollution, Urban noise, Machine Learning, Internet of Things