Author(s)

JEREMIAH A. IKWEN, LARISA V. KRUGLOVA, FRANKA A. UNDIE, ABIOLA E. AYODELE, ONYINYECHI GRACE OPARA

Abstract

Nigeria is one of Africa’s largest oil producers and the oil and gas sector contributes substantially to national revenue and export earnings. However, decades of intensive oil extraction in the Niger Delta have resulted in frequent oil spills and long-term ecological degradation. This study systematically reviews the application of artificial intelligence (AI), particularly predictive modelling techniques, for monitoring and forecasting ecological degradation in oil spill-affected areas of the Niger Delta. Using PRISMA-aligned review methods, peer-reviewed studies, policy reports and remote-sensing, datasets were analysed to assess current AI-based monitoring approaches and their effectiveness. The review indicates that machine-learning analysis of satellite imagery can accurately identify oil-induced environmental damage with repeated spill sites exhibiting significant declines in vegetation indices, such as NDVI that correlate with on-ground ecological degradation. Illustrative modelling using open-source satellite data and convolutional neural networks demonstrated high classification performance in distinguishing degraded from non-degraded areas, enabling spatial mapping of high-risk zones. Despite these advances, major constraints persist, including data quality limitations, sparse ground-truth validation, inadequate technical infrastructure and institutional barriers to adoption. Targeted recommendations are proposed to strengthen environmental data systems, build technical capacity, integrate AI into regulatory enforcement and promote community-inclusive monitoring. AI-driven ecological monitoring offers a pathway towards earlier detection, improved remediation planning and enhanced transparency in environmental governance. With appropriate policy support and sustained investment, AI-based predictive monitoring can contribute significantly to environmental sustainability and resilience in Nigeria’s oil-producing regions.

Keywords

artificial intelligence (AI), ecological monitoring, oil spills, Niger Delta, remote sensing, predictive modelling, environmental sustainability