GIS-Based Statistical Analysis for Evaluation of Landslide Susceptibility Mapping: a Case Study in Wacha-Mizan Road Section, Southwestern Ethiopia
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Date
2024-06
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Addis Ababa University
Abstract
This study was undertaken in the Bench Maji Zone along the Mizan Wacha-Mizan roadway in southwestern Ethiopia, approximately 562 km from the capital, Addis Ababa. This region is notably susceptible to landslides, causing significant risks to road infrastructure, agricultural lands, and residential areas. The primary aim of this research was to develop a landslide susceptibility map using the Information Value Model a bivariate statistical method.
This approach evaluates the influence of historical landslides on each factor contributing to slope instability.Key causative factors identified include slope morphometry, aspect, curvature, soil composition, groundwater seepage, land use, and proximity to streams. Data were meticulously gathered through field investigations and corroborated with secondary sources. The integration of these seven causative factors culminated in a comprehensive landslide hazard susceptibility map. Statistical analysis of the information value revealed that certain classes of causative factors such as colluvium deposits, convex slope curvature, groundwater flow traces, slopes ranging from 25-35 degrees, north-facing aspects, Built up Areas, and proximity within 100m of stream exhibited the highest correlation with landslide occurrences.
The resultant landslide susceptibility map delineates the study area into various degrees of risk categories: 20.7 km2 (24.92%) of the study area falls in low susceptiblity, 28.7km2 (34.49%) of the area falls in moderate susceptiblity (MS) class and 33.7km2 (40.59%) of the study area falls in high susceptiblity (HS) class.
In general, the susceptibility classes derived from this study are consistent with historical landslide data and the causative factors examined. The findings from the Information Value Model provide a reliable basis for informed infrastructure planning, ensuring enhanced safety within the study area.
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Bivariate Statistical Approach, Information Value Model, Landslide Susceptibility