LEVERAGING ARTIFICIAL INTELLIGENCE TO PREDICT AND REDUCE FLOOD IMPACTS ON CONSTRUCTION SITES: A CASE STUDY OF ONITSHA NORTH, ANAMBRA STATE
Abstract
Flooding poses a persistent threat to urban centers in Nigeria, with Onitsha North in Anambra State experiencing recurrent inundation that severely impacts infrastructure, commerce, and livelihoods. This study applied artificial intelligence (AI) to predict and mitigate flood risks on construction sites using the FastFlood simulation platform. Input data included a 20-meter resolution Digital Elevation Model (SRTM), CHIRPS rainfall intensity of 10 mm/hour over six hours, infiltration rates, and urban land cover parameters. The October 7, 2022 flood event was modeled, revealing that 65% of Onitsha North (32.5 km²) was inundated, with depths ranging from 0.5 to 1.2 meters and a mean depth of 1.0 meter. Vulnerability analysis identified high-risk zones such as Okpoko, Main Market, and key residential areas, with model outputs validated against National Emergency Management Agency (NEMA) and Civil Justice Initiative (CJI) 2022 reports. The findings highlight the disproportionate impact on commercial and transport corridors and underscore the importance of AI-driven flood prediction for evidence-based mitigation planning. Recommended measures include elevated foundations, drainage optimization, flood barriers, and AI-based early warning systems. Overall, the study demonstratedthe potential of AI modeling to enhance disaster preparedness, strengthen construction resilience, and inform sustainable urban planning in flood-prone Nigerian cities.
Keywords: Flood modeling; Artificial Intelligence, FastFlood, Construction sites, Flood vulnerability, Urban resilience, Onitsha North Nigeria
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