Alazar Shitaye Tiruneh

Mr. Alazar Shitaye Tiruneh


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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18 October 2022 in Applied Water Science
Verified
Journal Article
Open access
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ANAjanaw Negese
Ajanaw Negese
DADessalegn Worku Ayalew
Dessalegn Worku Ayalew
ATAlazar Shitaye Tiruneh
Alazar Shitaye Tiruneh
HGHaile Getnet
Haile Getnet
Flood is one of the natural hazards that causes widespread destruction such as huge infrastructural damages, considerable economic losses, and social disturbances across the world in general and in Ethiopia, in particular. Dega Damot is one of the most vulnerable districts in Ethiopia to flood hazards, and no previous studies were undertaken to map flood-prone areas in the district despite flood-prone areas identification and mapping being crucial tasks for the residents and decision-makers to reduce and manage the risk of flood. Hence, this study aimed to identify and map flood-prone areas in Dega Damot district, northwestern Ethiopia, using the integration of Geographic Information System and multi-criteria decision-making method with analytical hierarchy process. Flood-controlling factors such as elevation, slope, flow accumulation, distance from rivers, annual rainfall, drainage density, topographic wetness index, land use and land cover, Normalized Difference Vegetation Index, soil type, and curvature were weighted and overlayed together to achieve the objective of the study. The result shows that about 86.83% of the study area has moderate to very high susceptibility to flooding, and 13.17% of the study area has low susceptibility to flooding. The northeastern and southwestern parts of the study area dominated by low elevation and slope, high drainage density, flow accumulation, topographic wetness index, and cropland land use were found to be more susceptible areas to flood hazards. The final flood susceptibility map generated by the model was found to be consistent with the historical flood events on the ground in the study area, revealing the method’s effectiveness used in the study to identify and map areas susceptible to flood.