STAKEHOLDERS’ PERCEPTION OF GIS AND AI-DRIVEN NATURE-BASED SOLUTIONS FOR RESILIENCE AND MITIGATION IN THE FLOOD-VULNERABLE COMMUNITIES OF SOUTH WEST NIGERIA

Abstract

Flooding remains a major environmental challenge in southwest Nigeria due to extreme rainfall, rapid urbanization, deforestation, and inadequate drainage systems. In response to the limitations of conventional flood control measures, innovative approaches such as Geographic Information System (GIS) and Artificial Intelligence (AI)–driven Nature-based Solutions (NbS) are increasingly being considered. These technologies support the identification of flood-prone areas and the strategic deployment of natural infrastructure to enhance resilience and mitigate flood risks in vulnerable communities. This study employs a mixed-methods approach combining GIS spatial analysis, AI-based flood prediction models, and 50 stakeholders were interviewed to evaluate the effectiveness and barriers to utilization of these technologies in Lagos, Ibadan, and Abeokuta. A survey method was used to collect data from the stakeholders. Results show that GIS-based flood models significantly enhance real-time flood monitoring, while AI-driven NbS reduce runoff, improve urban resilience, and minimizes flood damage. Most stakeholders (24%) from agencies such as NiMET, NEMA, Urban Authorities, IUFMP, and NGOs, along with 16% of community leaders, reported awareness of GIS and AI-driven Nature-based Solutions (NbS) for flood management in Southwest Nigeria. However, an equal proportion (24%) noted poor GIS effectiveness in detecting flood risks and the limited usefulness of AI-NbS. Additionally, 30% of respondents identified inadequate flood management data as a major barrier. A large majority highlighted systemic constraints, including weak policy enforcement (82%), insufficient funding (84%), and limited community engagement (76%), all of which hinder the effective deployment of GIS/AI-NbS in the region. However, challenges like limited access to real-time hydrological data, weak policy enforcement, and financial constraints hinder full implementation. The study concludes by recommending integration of AI and GIS into Nigeria’s flood risk management systems through improved policy frameworks, strengthened data infrastructures, and increased stakeholder engagement.