Evaluating the Impact of the Spatial Resolution of Digital Elevation Models on Flood Modelling DOI
Ekundayo A. Adesina, Joseph Olayemi Odumosu, Oluibukun Gbenga Ajayi

et al.

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Language: Английский

A comprehensive review of flood monitoring and evaluation in Nigeria DOI

Babati Abu-hanifa,

Auwal F. Abdussalam, Saadatu Umaru Baba

et al.

International Journal of Energy and Water Resources, Journal Year: 2025, Volume and Issue: unknown

Published: April 8, 2025

Language: Английский

Citations

0

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

Desmond Rowland Eteh,

Bunakiye R. Japheth,

Charles Ugochukwu Akajiaku

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 18, 2025

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

Language: Английский

Citations

0

Evaluating the Impact of the Spatial Resolution of Digital Elevation Models on Flood Modelling DOI
Ekundayo A. Adesina, Joseph Olayemi Odumosu, Oluibukun Gbenga Ajayi

et al.

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Language: Английский

Citations

0