Artificial Intelligence Approach to Crude-Oil Exploitation and Environmental Issues in Coastal Areas of Nigeria
Keywords:
Artificial Intelligence, Crude-Oil Exploitation, Environmental Degradation, Sentinel-2Abstract
Crude oil exploitation in Nigeria’s Niger Delta contributes to environmental degradation and public-health risks, necessitating integrated approaches for monitoring ecological changes and identifying vulnerable communities. This study developed an artificial intelligence (AI)-driven framework for assessing environmental degradation and community health risks across 180 coastal incident settlements. Sentinel-2 Level-2A multispectral imagery, water contamination indices (WCI), geospatial proximity measures, and secondary community morbidity records were integrated to evaluate Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GBM), and Logistic Regression (LR) models. Performance was assessed using stratified five-fold cross-validation. SVM achieved 95.56% accuracy, 95.35% precision, 100% sensitivity, an F1-score of 0.976, and an area under the receiver operating characteristic curve (AUC-ROC) of 0.860. RF achieved 95.56% accuracy and an AUC-ROC of 0.902, while LR recorded an AUC-ROC of 0.927. Satellite-derived vegetation indices indicated substantial degradation, with Normalized Difference Vegetation Index (NDVI) values declining from pre-impact baselines of 0.38-0.44 to below 0.18 in severely degraded zones. Elevated WCI (>0.75) and limited healthcare accessibility were associated with respiratory morbidity (56.6 cases per 1,000) and dermatological disorders (46.8 cases per 1,000). The proposed framework offers decision-support potential for environmental monitoring, public-health surveillance, and regulatory interventions in petroleum-producing coastal communities.
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