Identifying flood vulnerable and risk areas using the integration of analytical hierarchy process (AHP), GIS, and remote sensing: A case study of southern Oromia region

Many people have died, and economies have been devastated by flooding, which is one of the world's most prevalent and catastrophic natural catastrophes. As a result of human activity on the land, the flooding patterns have changed. Inadequate drainage infrastructures, as well as various activities such as deforestation, are exacerbating the flood. In Ethiopia, southern Oromia is one of the regions affected by flooding, with the West Guji and Guji zones being the most frequently flooded places. People are exploiting natural vegetation for various purposes as a result of rapid population growth, resulting in deforestation and susceptibility to natural disasters such as floods. As a result, identifying flood-prone locations in great detail is essential for effective issue-solving. Thus, the study aims to locate and map flood-prone and flood-risk regions in southern Oromia utilizing Remote Sensing, analytical hierarchy Process (AHP), and Geographic Information system (GIS). Eight flood-causing factors, such as drainage density, rainfall, elevation, slope, Topographic wetness index (TWI), soil type, distance from the river, and LULC were developed in the GIS environment, and ERDAS Imagine software was employed to make land coverage and land usage data. The weighted overlay Multi-criteria evaluation (MCE) approach was then used to construct a flood-sensitive map based on the AHP pairwise comparison of eigenvector value. To determine flood risk, population density, a vulnerable flood map, and land usage/land coverage layer were overlaid, having equal weightage. The flooding vulnerability outcome in southern Oromia in-dicates that 0.3% (20.53km2), 31.4% (24,986.4km2), 62% (49,310.6km2), 6.39% (5081.98km2), and 0.19% (148.866) is, very high, high, moderate, low, and very low flooding vulnerable region correspondingly. From the flood risk map, it was estimated that 2% (1587.3km2), 19.3% (15,384.4km2), 75.5% (60,071.7km2), and 3.15% (2505.3km2) of the area were endangered to very high, high, moderate, and low, flood risk levels correspondingly. As a result, the study will assist the relevant authorities in formulating development policies based on the area's existing flood danger and risk.