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| Format: | Recurso digital |
| Sprog: | engelsk |
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Zenodo
2026
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| Online adgang: | https://doi.org/10.66050/xfxaaq83 |
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| _version_ | 1866901382019153920 |
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| author | Bashir, Ishaku Yakubu Abubakar, Sheik Danjuma Jiya, Solomon Ndace Muhammad, Yakubu Yakubu, Aisha Aliyu |
| author_facet | Bashir, Ishaku Yakubu Abubakar, Sheik Danjuma Jiya, Solomon Ndace Muhammad, Yakubu Yakubu, Aisha Aliyu |
| contents | <p>The flood risk in the Niger-East region of Niger State is increasingly becoming an annual event. Climatic<br>shifts, land-surface modifications, and human socioeconomic factors are among the conditions that trigger<br>floods. This study explores geospatial technology and multicriteria decision analysis-analytical hierarchy pro-<br>cess (MCDA-AHP) to develop a flood risk prediction system that leverages Google Earth Engine to process<br>remote sensing data directly influencing flood risk. Elevation, slope, drainage density, rainfall, soil, proximity<br>to drainage, proximity to road, population density, flow accumulation, and land use land cover (LULC). The<br>weightage assignment was performed using the MCDA-AHP technique. Flood risk classes predicted as very<br>low, 13.82 km2 (9.29%), low, 18.77 km2 (12.61%), low – moderate, 111.97 km2 (75.24%), high, 3.32 km2 (2.23%),<br>and very high, 0.93 km2 (0.63%) of the study area, respectively. This research presents a flood emergency re-<br>sponse system that highlights the impact of different prioritization criteria across multiple conditions. There-<br>fore, integrating GEE to generate different flood-conditioning risk indicators, prioritized and ranked using<br>MCDA-AHP, is crucial for developing an efficient methodological framework for flood risk prediction across<br>a wide region, achieving 88% precision. Thus, effective for evidence-based decision-making by authorities,<br>policy makers, and emergency response agencies</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_66050_xfxaaq83 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Unveiling an efficient framework for predicting flood risk areas, using Earth observatory data, Google Earth Engine, and multicriteria decision making-analytical hierarchy process Bashir, Ishaku Yakubu Abubakar, Sheik Danjuma Jiya, Solomon Ndace Muhammad, Yakubu Yakubu, Aisha Aliyu Google Earth Engine MCDA-AHP GIS remote sensing flood prediction Niger East <p>The flood risk in the Niger-East region of Niger State is increasingly becoming an annual event. Climatic<br>shifts, land-surface modifications, and human socioeconomic factors are among the conditions that trigger<br>floods. This study explores geospatial technology and multicriteria decision analysis-analytical hierarchy pro-<br>cess (MCDA-AHP) to develop a flood risk prediction system that leverages Google Earth Engine to process<br>remote sensing data directly influencing flood risk. Elevation, slope, drainage density, rainfall, soil, proximity<br>to drainage, proximity to road, population density, flow accumulation, and land use land cover (LULC). The<br>weightage assignment was performed using the MCDA-AHP technique. Flood risk classes predicted as very<br>low, 13.82 km2 (9.29%), low, 18.77 km2 (12.61%), low – moderate, 111.97 km2 (75.24%), high, 3.32 km2 (2.23%),<br>and very high, 0.93 km2 (0.63%) of the study area, respectively. This research presents a flood emergency re-<br>sponse system that highlights the impact of different prioritization criteria across multiple conditions. There-<br>fore, integrating GEE to generate different flood-conditioning risk indicators, prioritized and ranked using<br>MCDA-AHP, is crucial for developing an efficient methodological framework for flood risk prediction across<br>a wide region, achieving 88% precision. Thus, effective for evidence-based decision-making by authorities,<br>policy makers, and emergency response agencies</p> |
| title | Unveiling an efficient framework for predicting flood risk areas, using Earth observatory data, Google Earth Engine, and multicriteria decision making-analytical hierarchy process |
| topic | Google Earth Engine MCDA-AHP GIS remote sensing flood prediction Niger East |
| url | https://doi.org/10.66050/xfxaaq83 |