2 research outputs found
Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran
The main objective of this study is to investigate
potential application of frequency ratio (FR), weights of
evidence (WoE), and statistical index (SI) models for
landslide susceptibility mapping in a part of Mazandaran
Province, Iran. First, a landslide inventory map was constructed
from various sources. The landslide inventory map
was then randomly divided in a ratio of 70/30 for training
and validation of the models, respectively. Second, 13
landslide conditioning factors including slope degree, slope
aspect, altitude, plan curvature, stream power index, topographic
wetness index, sediment transport index, topographic
roughness index, lithology, distance from streams,
faults, roads, and land use type were prepared, and the
relationships between these factors and the landslide
inventory map were extracted by using the mentioned
models. Subsequently, the multi-class weighted factors were
used to generate landslide susceptibility maps. Finally, the
susceptibility maps were verified and compared using several
methods including receiver operating characteristic
curve with the areas under the curve (AUC), landslide
density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies