Volume 2, Issue 3, January - June, 2025

Machine learning-based flood extent mapping and damage assessment in Yenagoa, Bayelsa State, using Sentinel-1 and 2 imagery (2018-2022)

Becky P Bamiekumo^1♦, Emmanuella O Akpobome¹, Ayanka N Kemebaradikumo², Omabuwa O Mene-Ejegi³, Desmond R Eteh¹

¹Department of Geology, Niger Delta University, Wilberforce Island, Bayelsa State, Nigeria
²Department of Geology, University of Port Harcourt, Rivers State, Nigeria
³Department of Environmental Management and Control, Rivers State University, Port-Harcourt, Rivers State, Nigeria

^♦Corresponding Author
Department of Geology, Niger Delta University, Wilberforce Island, Bayelsa State, Nigeria

ABSTRACT

The study utilizes the application of Sentinel-1 and Sentinel-2 satellite imagery between 2018 and 2022 in assessing flood extent and damages in Yenagoa, Bayelsa State, Nigeria, using machine learning techniques applied to Sentinel-1 and Sentinel-2 satellite imagery from 2018 to 2022. Synthetic Aperture Radar (SAR) data from Sentinel-1, with its all-weather capabilities, enabled the detection and mapping of the flood extent using machine learning algorithms, while Sentinel-2 multispectral images facilitated land-use classification before and after the flood events using support vector machines (SVM). The Shuttle Radar Topographic Mission (SRTM) and geological map were also used. This study, carried out using Google Earth Engine (GEE), Python, JavaScript, and ArcGIS 10.5, reveals a tremendous increase in the flood-affected areas, which expanded from 54.92 km² in 2018 to 90.15 km² in 2022. It found that the main drivers of such events were increased rainfall and rapid urbanization. The DEM data extracted from SRTM showed that the low-lying areas, specifically those with an elevation range of -6 m to 7 m (gentle slope, range from 1° to 9°), are the areas most prone to flooding. The geological composition, described as a swampy deltaic plain, contributed to prolonging the duration and severity of the flood. Machine learning analysis using Sentinel-2 imagery showed that vegetated and built-up classes are highly flooded, thus bringing socio-economic losses due to displacement of households and economic loss. This study has brought out the vital role of machine learning and remote sensing in flood detection and monitoring, besides the urgent need for data-driven flood risk management strategies integrating regional topography, land use dynamics, and geological factors.

Keywords: Flood extent, machine learning, GEE, Yenagoa, flood damage, Sentinel- 1/2

Discovery Nature, 2025, 2(3), e2dn1041

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DOI: https://doi.org/10.54905/disssi.v2i3.e2dn1041

Published: 12 January 2025