The main objective of this study is to investigate\ud
potential application of frequency ratio (FR), weights of\ud
evidence (WoE), and statistical index (SI) models for\ud
landslide susceptibility mapping in a part of Mazandaran\ud
Province, Iran. First, a landslide inventory map was constructed\ud
from various sources. The landslide inventory map\ud
was then randomly divided in a ratio of 70/30 for training\ud
and validation of the models, respectively. Second, 13\ud
landslide conditioning factors including slope degree, slope\ud
aspect, altitude, plan curvature, stream power index, topographic\ud
wetness index, sediment transport index, topographic\ud
roughness index, lithology, distance from streams,\ud
faults, roads, and land use type were prepared, and the\ud
relationships between these factors and the landslide\ud
inventory map were extracted by using the mentioned\ud
models. Subsequently, the multi-class weighted factors were\ud
used to generate landslide susceptibility maps. Finally, the\ud
susceptibility maps were verified and compared using several\ud
methods including receiver operating characteristic\ud
curve with the areas under the curve (AUC), landslide\ud
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
