Predictive Modeling of Urban Flood Risk Zones in Nigerian Cities Using Hybrid AI and Socioeconomic Data

Urban flooding remains one of the most pressing environmental and socioeconomic challenges in Nigeria, particularly in rapidly growing cities such as Lagos, Port Harcourt, and Ibadan. Conventional flood prediction approaches, often limited to hydrological and meteorological data, fail to capture the complexity introduced by urbanization, socioeconomic inequalities, and inadequate infrastructure. To address these gaps, this study develops a hybrid Artificial Intelligence (AI) framework that integrates spatial imagery with socioeconomic and climatic variables to improve urban flood risk prediction. The methodology combines Convolutional Neural Networks (CNNs) for analyzing geospatial and satellite imagery with Gradient Boosting Machines (GBMs) for modeling non-visual features, including poverty index, housing density, and rainfall intensity. A meta-learner ensemble strategy, using logistic regression, was employed to optimally fuse the predictions from both models. Comparative experiments were conducted to evaluate CNN-only, GBM-only, and hybrid ensemble models across multiple Nigerian cities, followed by visualization through flood risk maps and feature importance rankings. The findings demonstrate that the hybrid ensemble significantly outperformed individual models, achieving higher prediction accuracy and generalization. The integration of socioeconomic factors not only improved the model’s sensitivity to high-risk zones but also revealed critical drivers of vulnerability, such as unplanned housing and poor drainage systems. Case studies on Lagos Island and Port Harcourt showed that the hybrid model provided more realistic and actionable predictions compared to hydrology-only approaches. Flood risk maps effectively identified high, medium, and low-risk areas, offering valuable insights for targeted disaster response. This research highlights the potential of AI- driven hybrid modeling as a transformative tool for urban flood management in Nigeria. By integrating geospatial and socioeconomic intelligence, the framework enables data-informed policymaking, urban planning, and disaster preparedness. Future work should prioritize real-time flood alert systems and mobile-based decision support tools, ensuring that predictive insights translate into timely, community-level action.