Predictive Modeling Using AI for Monitoring Ecological Degradation in Oil Spill Zones in the Niger Delta
Department of Oil and Gas engineering, Academy of Engineering, RUDN University, Moscow, Russia.
Review
International Journal of Science and Technology Research Archive, 2025, 09(02), 044-063.
Article DOI: 10.53771/ijstra.2025.9.2.0066
Publication history:
Received on 18 November 2025; revised on 24 December 2025; accepted on 26 December 2025
Abstract:
Nigeria, as one of the largest oil producers in Africa, plays a crucial role in the global energy market. The oil and gas sector contributes significantly to the country’s GDP, provides direct and indirect employment, and serves as a major source of government revenue. However, this critical sector is plagued by persistent challenges such as corruption, insecurity, environmental degradation, and escalating community conflicts—particularly in the Niger Delta region. These issues not only threaten the sustainability and future growth of the sector but also result in severe ecological consequences that undermine the livelihoods and health of local communities. This research paper examines how Artificial Intelligence (AI), specifically predictive modeling techniques, can be employed to monitor and forecast ecological degradation in oil spill zones across the Niger Delta. Using a qualitative approach, the study analyzes secondary data from environmental reports, satellite imagery, and case studies to understand the dynamic interplay between oil exploration activities and ecological outcomes. It also explores the roles and responsibilities of stakeholders—including the Nigerian government, multinational oil companies, and local communities—in both the problem and its potential solutions. The paper emphasizes the transformative potential of AI in addressing environmental challenges by enabling early detection of degradation, efficient allocation of remediation resources, and more transparent environmental monitoring. Furthermore, the research highlights broader opportunities for Nigeria to diversify its economy and invest in renewable energy alternatives, which could reduce its overdependence on fossil fuels and mitigate ecological risks. To fully realize these benefits, the study underscores the urgent need for policy reforms that promote transparency, strengthen regulatory frameworks, and ensure meaningful community participation. Ultimately, the integration of AI in environmental monitoring presents a viable pathway toward more sustainable development in Nigeria's oil-rich regions.
Keywords:
Artificial Intelligence (AI); Predictive Modeling; Oil Spills; Ecological Degradation; Niger Delta; Environmental Monitoring; Nigeria; Oil and Gas Sector; Sustainable Development; Remote Sensing
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