Unveiling an efficient framework for predicting flood risk areas, using Earth observatory data, Google Earth Engine, and multicriteria decision making-analytical hierarchy process

The flood risk in the Niger-East region of Niger State is increasingly becoming an annual event. Climatic
shifts, land-surface modifications, and human socioeconomic factors are among the conditions that trigger
floods. This study explores geospatial technology and multicriteria decision analysis-analytical hierarchy pro-
cess (MCDA-AHP) to develop a flood risk prediction system that leverages Google Earth Engine to process
remote sensing data directly influencing flood risk. Elevation, slope, drainage density, rainfall, soil, proximity
to drainage, proximity to road, population density, flow accumulation, and land use land cover (LULC). The
weightage assignment was performed using the MCDA-AHP technique. Flood risk classes predicted as very
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%),
and very high, 0.93 km2 (0.63%) of the study area, respectively. This research presents a flood emergency re-
sponse system that highlights the impact of different prioritization criteria across multiple conditions. There-
fore, integrating GEE to generate different flood-conditioning risk indicators, prioritized and ranked using
MCDA-AHP, is crucial for developing an efficient methodological framework for flood risk prediction across
a wide region, achieving 88% precision. Thus, effective for evidence-based decision-making by authorities,
policy makers, and emergency response agencies