Application of frequency ratio, statistical index, and weights-of-evidence models and
their comparison in landslide susceptibility mapping in Central Nepal Himalaya
ABSTRACT
The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones
of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and
folds. Over the years, this road section and its surrounding area have experienced repeated
landslide activities. For that reason, landslide susceptibility zonation is essential for roadside
slope disaster management and for planning further development activities. The main goal of
this study was to investigate the application of the frequency ratio (FR), statistical index (SI),
and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road
section and its surrounding area. For this purpose, the input layers of the landslide
conditioning factors were prepared in the first stage. A landslide inventory map was prepared
using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of
438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly
selected as training data for the modeling using FR, SI, and WoE models and the remaining
143 (33 %) were used for the validation purposes. The landslide conditioning factors
considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream
power index, topographic wetness index, lithology, land use, distance from faults, distance
from rivers, and distance from highway. The results were validated using area under the
curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of
76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %;
predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %)
models. Overall, all the models showed almost similar results. The resultant susceptibility
maps can be useful for general land use planning.
Keyword:
Landslides; Frequency ratio; Statistical index; Weights-of-evidence; GIS;
Himalaya