Abstract:
The study area in northwestern Ethiopia is one
of the most landslide-prone regions, which is characterized by frequent high landslide occurrences. To predict
future landslide occurrence, preparing a landslide susceptibility mapping is imperative to manage the landslide
hazard and reduce damages of properties and loss of
lives. Geographic information system (GIS)-based
frequency ratio (FR), information value (IV), certainty factor
(CF), and logistic regression (LR) methods were applied.
The landslide inventory map is prepared from historical
records and Google Earth imagery interpretation. Thus,
717 landslides were mapped, of which 502 (70%) landslides were used to build landslide susceptibility models,
and the remaining 215 (30%) landslides were used to
model validation. Eleven factors such as lithology, land
use/cover, distance to drainage, distance to lineament,
normalized difference vegetation index, drainage density,
rainfall, soil type, slope, aspect, and curvature were evaluated and their relationship with landslide occurrence was
analyzed using the GIS tool. Then, landslide susceptibility
maps of the study area are categorized into very low, low,
moderate, high, and very high susceptibility classes. The
four models were validated by the area under the curve
(AUC) and landslide density. The results for the AUC are
93.9% for the CF model, which is better than 93.2% using
IV, 92.7% using the FR model, and 87.9% using the LR
model. Moreover, the statistical significance test between
the models was performed using LR analysis by SPSS software. The result showed that the LR and CF models have
higher statistical significance than the FR and IV methods.
Although all statistical models indicated higher prediction
accuracy, based on their statistical significance analysis
result (Table 5), the LR model is relatively better followed
