Extended Abstract
Introduction
Due to mainly mountanous topography, specific geographical conditions, various geological formations, neo tectonical activities, and seismicity, Iran is potantially prone to landslides.Gilan and Roudbar region in the southern part of this province are among areas potentially susceptibleof landslides, rock falls, and other events associated with seismichillsides. Landslide results in severe erosions and sometimes leads to financial losses or loss of lives. Therefore, it is important to investigate the causes behind this phenomenon and determinezones prone to land sliding. 
 Materials and methods
In this study, we tried to usea sample of trenches and boundaries offaultslocated inRoudbar sliding slopes in order to characterize the sliding soils. Following this field investigatation,soil samples were obtained from 10 landslide zones.  Then, factors affecting the sliding slopes were identified and a digital map was produced for each factor. Nine data layers including direction and degree of slopes, geology, landuse, precipitation, relative changes in elevation, distance from roads, rivers and faults were used in GIS environment to prepare the weighted maps.  Afterwards, LNSF statistical method was used for data analysis in GIS environment and the study area was divided into 5 zones with very low (1), low (2), moderate (3), high (4), and very high (5) sliding susceptibility.  Following the integration and analysis of layers using LNSF model, 26 zonation mapswere calculated, and the best map was selected using success rate curves.  Then, the zone with highest potentiality for landslide occurrence was selected for further studies from the five zones mentioned before.  Hydrometry, Atterberg limits and direct shear tests were performedin the Soil Mechanics Laboratory of Gilan University with the aim of identifying physical and mechanical properties of soil samples.
 Results and discussion
Results indicate that with LNSF method, it is possible tozone a vast area (12814.2 hectaresin this research) based on landslide potentiality and then focus on the most critical area (very high landslide potentiality) toinvestigate factors and conditions resulting in the occurrence of landslides or prevention strategies. Success rate charts helps us to determine the most optimal landslide zoning map (i.e. a map inwhich the highest percentage of landslide pixels occur in the “very high potentiality” zone). Following the selection of final zonebased on success rate graphs, from the 26 zoning maps, it was concluded that the ​​landslide zone with very high potentiality encompasses 282.6825 hectares or 2.2% of the total area under study.At the weighting stage, the highest weight was allocated to the seventh category of the land use layer, which at the final zoning stage covers nearly the whole area with very high potantiality of landslides. Therefore, there is a direct relation between the allocated weight in the subject categories and the percentage of its occupancy level in the final zoning.Zoning the results of granulation experiments by Thiessen Polygon, it was concluded that CL type soil coversnearly half of the area with very high landslide potentiality. Determining the static reliability coefficient of the area with very high ​​landslide potentiality, we found that in case soil reaches saturation, unstability of hillsidesin a large part of the study areacanbe expected.
 Conclusion
Dispersion of landslides in Iran is mainly concentrated in Southern Gilan Province. Based on the investigation of the situations in the study area,geology, landuse, distance from highway are identified as the most affective factors in theoccurance of landslides.Following the weighting stage with LNSF method, rated layers were prepared in GIS enviornment, final overlapping was performed, and landslide zoning map of the study area was produced. Based on the landslide risk zonning map, the study area was divided into 5 subsections: 2.21% of the study area had very high sensetivity, 26.43% high sensetivity, 42.28% avarage sensetivity, 25.25% low sensetivity, and 3.83% very low sensetivity. Considering the zonning map produced, it properly overlaps with identified landslides in the area, and help governmental policy makings. It specifically helps Organization of Roads in construction of new roads.