BEYOND FLOOD SUSCEPTIBILITY MAPPING: INTEGRATING GIS, REMOTE SENSING AND MACHINE LEARNING FOR FLOOD-RISK ASSESSMENT IN NIGERIA 0 0
DOI:
https://doi.org/10.83374/jesber.vol1no2.93Keywords:
Flood susceptibility, Flood-risk assessment, Geographic Information Systems (GIS), Remote sensing, Machine learning, GeoAI, Vulnerability assessment, NigeriaAbstract
Flooding is a persistent environmental hazard in Nigeria, affecting settlements, infrastructure, agriculture, and livelihoods. Geographic Information Systems (GIS), remote sensing, and Machine Learning (ML) have improved the identification of flood-prone locations and the prediction of spatial flood patterns, yet a recurrent conceptual problem remains: a flood-susceptibility map is not, by itself, a flood-risk assessment. This paper presents a structured critical review of Nigerian flood studies published principally between 2016 and 2026, evaluating conceptual framing, data sources, modelling methods, and treatment of hazard, exposure, vulnerability, and uncertainty. The review finds that the literature has evolved from GIS-based multi-criteria mapping through statistical and ML modelling to event-scale satellite mapping and emerging integrated GeoAI approaches, with technical capability advancing faster than risk integration: many studies still estimate relative spatial propensity while omitting inundation depth, exposed assets, social vulnerability, and consequence. The review's main contribution is an operational framework in which dominant flood processes are diagnosed before modelling, hazard is characterised from satellite and hydrodynamic evidence, and susceptibility is explicitly linked to exposure, vulnerability, and future climate and land-use change. Future Nigerian flood studies should be judged not only by predictive accuracy, but also by physical plausibility, generalisability, uncertainty communication, and usefulness for planning.Downloads
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