Abstract:
Goncha Siso Eneses area of East Gojam Zone in northwestern Ethiopia is one of the most landslide-prone regions, which
is characterized by frequent landslide occurrences causing fatalities and damages in cultivated and non-cultivated lands,
infrastructure and properties. Hence, preparing a landslide susceptibility map is very helpful in reducing the damages in
infrastructure and properties and loss of animal and human lives. In this study, GIS-based information value and logistic
regression models were applied. A reliable and detailed landslide inventory with 894 landslides was prepared through
detailed fieldwork and Google Earth image interpretation. These landslides were randomly divided into training data set
for model development and testing data set for model validation. Nine landslide causative factors like slope, curvature,
aspect, lithology, distance to stream, distance to lineament, distance to spring, rainfall and land use/cover were integrated
with training landslides to determine the weight(s) of each landslide factor and factor classes using Information Value
and Logistic Regression models, respectively. The landslide susceptibility index map was then produced by summing the
weights of all the landslide factors using raster calculator of the spatial analyst tool in GIS. To evaluate the performance of
the information value and logistic regression models for landslide susceptibility modeling, the relative landslide density
index and area under the curve (AUC) of the receiver operating characteristic curves were performed on both the training and testing landslide data sets. The model has an AUC accuracy of 88.9% success rate and 85.9% prediction rate for
information value model whereas 81.8% success rate and 80.2% predictive rate for logistic regression model.
