Prediction of potential gold location using machine learning techniques :

Abstract

This study explores the use of machine learning techniques in mineral exploration, specifically in predicting potential locations of gold mineralization in Busia District, Uganda. The study uses geological data, including lithology, geophysical data, geochemical data as well and geographical data to develop and test a predictive model. The results show that the developed model can accurately predict areas with potential gold mineralization, with an accuracy of 87.5%. The study's significance lies in its potential to reduce the time and cost of identifying new gold mineralized areas, contributing to sustainable consumption and production patterns, and promoting sustained, inclusive, and sustainable economic growth. The study recommends validating the accuracy of predictive models, exploring the use of non-geological data sources, monitoring and mitigating the environmental impacts of gold mining, and sharing the findings of this study with relevant stakeholders.