Next-Generation AI Architecture for Real-Time Hazard Prediction and Safety Optimization in Mining Systems

Abstract

The mining industry is a risky sphere of industry that is characterized by unstable geological conditions, the dangerous environment, and the active use of machinery. Traditional safety systems are based on manual surveillance and limits-like warnings, which are reactive in nature and cannot be used to mitigate the risk early enough. This paper suggests a next-generation AI system that can be used to predict hazards on-site and optimize safety in mining systems. The framework combines IoT-permitted environmental sensing, computer vision, and sophisticated machine learning models to continuously determine the level of gases, the structural integrity, machine well-being, and workers. The deep learning is also used in estimating non-destructive ore quality by using image-based mineral analysis, which facilitates effective resource management. Long short-term memory networks (LSTM) and Autoencoders are predictive models that learn and identify anomalies, predict possible failures, and calculate a dynamic risk index. The analytics dashboard is a cloud-driven solution with a hierarchy of alerts that allow proactive action to be taken. The accuracy in hazard detection and ore prediction is high in an experimental result and has a significant improvement in accuracy compared to traditional systems. The proposed architecture will contribute to the operational safety, efficiency, and sustainability and will lead to intelligent and autonomous mining ecosystems.