Abstract
The purpose of the current study is to produce
landslide susceptibility maps using different probabilistic
and bivariate statistical approaches; namely, frequency
ratio (FR), weights-of-evidence (WofE), index-of-entropy
(IofE), and Dempster–Shafer (DS) models, at Wadi Itwad,
Asir region, in the southwestern part of Saudi Arabia.
Landslide locations were identified and mapped from in-
terpretation of high-resolution satellite images, historical
records, and extensive field surveys. In total, 326 landslide
locations were mapped using ArcGIS and divided into two
groups; 75 % and 25 % of landslide locations were used for
training and validation of models, respectively. Twelve
layers of landslide-related factors were prepared, including
altitude, slope degree, slope length, topography wetness
index, curvature, slope aspect, distance from lineaments,
distance from roads, distance from streams, lithology,
rainfall, and normalized difference vegetation index. The
relationships between the landslide-related factors and the
landslide inventory map were calculated using different
statistical models (FR, WofE, IofE, and DS). The model
results were verified with landslide locations, which were
operating characteristic curves were applied, and area un-
der the curve (AUC) was calculated for the different sus-
ceptibility maps using the success (training data) and
prediction (validation data) rate curves. The results showed
that the AUC for success rates are 0.813, 0.815, 0.800, and
0.777, while the prediction rates are 0.95, 0.952, 0.946, and
0.934 for FR, WofE, IofE, and DS models, respectively.
Subsequently, landslide susceptibility maps were divided
into five susceptibility classes, including very low, low,
moderate, high, and very high. Additionally, the percentage
of training and validating landslides locations in high and
very high landslide susceptibility classes in each map were
calculated. The results revealed that the FR, WofE, IofE,
and DS models produced reasonable accuracy. The out-
comes will be useful for future general planned develop-
.ment activities and environmental protection