Problem statement:The study area is frequently subjected to landslides especially alongside the E-W high way. This study demonstrates the implementation of the Frequency Ratio (FR) model with the aid of GIS tool to assess the contribution of every predisposing factor on landslides and produce a landslide susceptibility map to the area. Approach: A landslide location map of the study area was prepared based on the interpretation of aerial photographs, previous landslide maps and field surveys. In addition, ten relevant thematic maps representing considered factors were extracted from the constructed spatial database. Slope gradient, slope aspect, elevation, distance to road and drainage density, all were calculated from the topographic database; lithology, strata dip map and foliation dip map were generated from the geologic database; lineament density was prepared from Landsat-7 (ETM+) satellite image; and a soil map was prepared from the soil database. Results: relationship between landslides and instability factors was statistically evaluated by frequency ratio analysis. The results suggested that distance to road, lineament density and slope gradient are the most important factors effecting landslides. Frequency ratio values were used to produce the Landslide Susceptibility Index (LSI) with which the study area was divided into five zones of relative landslide susceptibility. Conclusion: The results of the analysis have been validated by calculating the AUC which shows an accuracy of 88.31% in the case of success rate curve and 84.68 % in the case of the prediction rate curve, indicating a high quality susceptibility map obtained from the FR model. The map could be used by decision makers as basic information for slope management and land use planning.

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In the literatures, discussions on the accuracy of different models for landslide analysis have been discussed widely. However, to date, arguments on the type of input data (landslides in the form of point or polygon) and how they affect the accuracy of these models can hardly be found. This study assesses how different types of data (point or polygon) applied to the same model influence the accuracy of the model in determining areas susceptible to landsliding. A total of 137 landslides was digitised as polygon (areal) units and then transformed into points; forming two separate datasets both representing the same landslides within the study area. These datasets were later separated into training and validation datasets. The polygon unit dataset uses the area density technique reported as percentage, while the point data uses the landslide density technique, as means of assigning weighting to landslide factor maps to generate the landslide susceptibility map that is based on the analytical hierarchy process (AHP) model. Both data groups show striking differences in terms of mapping accuracy for both training and validation datasets. The final landslide susceptibility map using area density (polygon) as input only has 48% (training) and 35% (validation) accuracy. The accuracy for the susceptibility map using the landslide density as input data achieved 89% and 82% for both training and validation datasets, respectively. This result showed that the selection of the type of data for landslide analysis can be critical in producing an acceptable level of accuracy for the landslide susceptibility map. The authors hope that the finding of this research will assist landslide investigators to determine the appropriateness of the type of landslide data because it will influence the accuracy of the final landslide potential map.