Modelling the spatial dynamics of flood inundation in Bayelsa State, Nigeria, using remote sensing and machine learning techniques

Flooding is a persistent environmental challenge in Bayelsa State, Nigeria, driven by low-lying terrain, high rainfall, and inadequate drainage systems. This study evaluates flood susceptibility in the Lower Niger Basin using a geospatial modelling approach. Ten flood-conditioning factors; rainfall, drainage density, slope, elevation, soil texture, land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), and proximity to rivers were integrated within a Random Forest (RF) framework. Spatial datasets were processed using ArcGIS and Google Earth Engine, and the model was trained with historical flood and non-flood locations to produce a high-resolution susceptibility map. Results indicate that areas receiving 2500 to 3199 mm of annual rainfall, particularly Southern Ijaw, Ekeremor, and Brass, exhibit the highest flood susceptibility. Over 82% of the region is characterized by low slopes (0 to 3°) and elevations ranging from 16 to 41 m, increasing flood vulnerability. High drainage density zones (up to 0.677 km/km²) contribute to flash flooding, while clay and sandy clay loam soils (covering > 60% of the area) reduce infiltration and enhance runoff. Built-up areas and regions with NDVI < 0.1 are also associated with higher flood risk. The RF model achieved an accuracy of 91%, with high-risk zones predominantly located within 1 km of major rivers. This study demonstrates the effectiveness of integrating machine learning with geospatial analysis for flood susceptibility mapping in data-scarce, low-lying environments. The resulting map provides a reliable decision-support tool for flood risk management, land-use planning, and targeted mitigation strategies in Bayelsa State.