Codular Waste Monitoring System for Organic and Non-Organic Waste Based on Internet of Things

This study aimed to identify characteristic properties of bedrock mineralization in the Vetreny Poyas Ridge, Russia, and develop an automated model to forecast gold-sulfide and gold-sulfide-quartz ore deposits based on geophysical and geochemical data integration.The research employed a combination of remote sensing, digital terrain modeling (DTM), geophysical potential fields, and discriminant analysis.Machine learning algorithms were applied to detect patterns in geodynamic zones, structural formations, and mineral occurrences.The chain fraction method was utilized for analytical continuation to enhance the predictive model's resolution.The findings confirmed that gold-sulfide mineralization correlates with discordant intersections of geodynamic zones and structural features.The predictive model successfully localized several high-potential mineral zones in the central and southeastern parts of the study area.Geochemical testing verified these findings, with significant gold anomalies aligning with predicted zones.The study demonstrates the potential of integrating AI-driven models with geophysical and geochemical data for enhanced mineral exploration.This method improves the accuracy of predicting mineralized zones in complex geological environments and can be adapted for use in other regions.