AI-BASED MINING INTERNAL POLLUTION CONDITION OBSERVATION AND NOTIFICATION SYSTEM
Keywords:
Artificial Intelligence, Mining Pollution, IoT Sensors, Environmental Monitoring, Edge Computing, Real-time Notification Systems, Deep Learning, Particulate Matter Detection, Mining Safety, Predictive Analytics.Abstract
This research paper presents an innovative artificial intelligence-based system for monitoring and providing real-time notifications regarding internal pollution conditions in mining environments. The mining industry faces significant challenges related to air quality management, particulate matter control, and toxic gas detection that directly impact worker health and operational efficiency. The proposed system utilizes a network of IoT sensors, deep learning algorithms, and edge computing to create a comprehensive monitoring framework that can detect, analyze, and predict hazardous pollution levels within underground and open-pit mining operations. Testing conducted across three active mining sites demonstrated that the AI-based system achieved 94.7% accuracy in pollution detection with response times averaging less than 30 seconds—significantly outperforming traditional monitoring approaches. The implementation of this system resulted in a 37% reduction in pollution-related incidents and a 42% improvement in evacuation response times during hazardous events. This research contributes to the growing field of smart mining technologies and provides a scalable solution for enhancing safety protocols and environmental compliance in mining operations worldwide.