INTEGRATING GEOSPATIAL TECHNOLOGIES AND MACHINE LEARNING FOR MONITORING AND ASSESSING ENVIRONMENTAL IMPACTS OF MINING ACTIVITIES IN THE SOUTH EAST OF NIGERIA: A STRUCTURED REVIEW

Abstract

The environmental impacts of mining activities in Southeast Nigeria pose significant challenges and threats to the local ecosystems and communities. Research reveals that the impact of these activities had hitherto been poorly monitored or assessed due to the inefficient manual approach used. Now, there are currently gaps in the literature on the potential of advanced technologies for sustainable environmental management vis-à-vis the mining practices in the Southeast and Nigeria. This research therefore seeks to bridge this gap and has adopted a review of the literature method to synthesize existing knowledge and determine the prospects and potential of the integration of geospatial technologies and machine learning for monitoring and managing these environmental impacts and support improved decision-making. This review adhered to the PRISMA guidelines, which involved an initial evaluation of 550 articles and which eventually resulted in 64 relevant materials used for this study. The findings indicate that mining activities led to severe land degradation, deforestation, and water contamination, adversely affecting biodiversity and local livelihoods. The study also revealed that advanced geospatial and machine learning technologies hold great potential for environmental monitoring, assessment, and management in the Southeast of Nigeria and Nigeria as a whole and call for urgent policy considerations from relevant stakeholders and governments.