Assessing the impact of climate change on flood patterns in downstream Nigeria using machine learning and geospatial techniques (2018-2024)

Abstract Climate change has increased flood risks in downstream Nigeria, driven by altered hydrology, dam operations, and land-use changes threatening infrastructure, livelihoods, and ecosystem stability with growing frequency and severity. This study analyzes flood patterns, identifies key environmental drivers, and predicts flood-prone areas through an integrated machine learning and geospatial analysis approach. Data sources included Synthetic Aperture Radar (SAR) imagery from Sentinel-1, rainfall measurements, Shuttle Radar Topography Mission (SRTM) elevation data, and surface water level records. Machine learning models Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) were applied using geospatial tools such as Google Earth Engine and ArcGIS 10.5 to assess flood dynamics from 2018 to 2024. Downstream regions (elevation: 78–235.1 m) exhibited greater flood susceptibility than upstream areas (up to 1399.43 m). Flood extents rose by 10.9% in August (from 2441.91 km² in 2018 to 2707.75 km² in 2024) and by 39.8% in October (from 3083.44 km² to 4311.55 km²). The RF model achieved the highest accuracy (92%), outperforming SVM (88%) and ANN (85%). Inundated areas increased from 20–35% of downstream zones. Rainfall intensity rose by 15–20%, with annual totals exceeding 4311 mm in some areas. Forest cover declined by 15–20%, further exacerbating flood risks. The findings demonstrate that climate change, land-use alteration, and dam operations are major contributors to flooding. Mitigation strategies include 10–15% reforestation, embankment construction, and machine learning–driven early warning systems, which can reduce flood damage by up to 30%. These approaches support sustainable flood risk management in Nigeria.