Evaluation of QS Method for Determination of Optimal Gamma in Landslide Risk Mapping (Case Study: Tuskestan forest to Gorgan)

Landslides are among the most dangerous and hazardous natural hazards in mountainous areas, which are mainly caused by earthquakes or rains, destroying people's lives and property every year. The increasing population and expansion of human settlements in mountainous areas, the difficulty of predicting the time of landslide occurrence, and the numerous factors contributing to the occurrence of this phenomenon, reveal the necessity of landslide hazard zoning.Landslide hazard zoning is crucial for quick and safe actions and strategic planning for the future. Therefore, scientific study of landslide phenomena and risk zoning mapping are important on the one hand to identify landslide potential areas in human activities area and on the other hand to identify safe locations for development. New habitats or other human uses such as roads, power and energy paths, power plants, etc., are of interest to planners at various scales.Golestan province in the north of Iran, with 200 registered mass movements, is one of the most active landslide areas, which is not excluded from the study area between Tuskestan forest to Gorgan. The present study, while zoning the landslide risk in the study area, evaluates and compares fuzzy operator zoning methods using two methods (Qs) and (P), has provided a suitable model for the assessment and evaluation of landslide risk. Therefore, the main purpose of this study is to select an appropriate and optimal model of fuzzy set of operators for mapping landslide hazard in the study area.

The zoning and preparation of landslide hazard maps in this study is based on the integration of landslides with effective criteria in landslide events. Initially, the landslide distribution data were collected in the study area, then transformed into sliding zones using high spatial resolution satellite imagery and Google Earth images.The layer obtained from landslide zones as the most important layer used in the present study is the dependent variable in the implementation of zoning models. Then, the parameters affecting the occurrence of landslides in the distance between Tuskestan forest to Gorgan were identified. Effective layers include elevation, slope, aspect, distance to road, geology, distance to river, land use and vegetation extracted from maps and images and then processed using various functions in Arc GIS software.After preparing these layers as effective variables, they were integrated with the landslide layer. integration and overlap layers were performed using the gamma function, which is a combination of multiplication and multiplication fuzzy multiplication, using the Raster Calculator tool in Arc Map environment. Next, the relative weight of each factor and its related classes was calculated using frequency ratio model. Then, after determining the final weights of the layers, by crossing the zone landslide map and hazard zoning maps, we evaluate and compare landslide hazard zoning methods using the Quality Sum Method and (Qs) and accuracy (P) were investigated and a suitable model was selected according to the study area.

Investigation of the results of fuzzy membership values (frequency ratio) and relationship between factors affecting landslide occurrence and landslides occurred in the study area shows that the lowest and highest altitudes were highest and lowest, respectively. The fuzzy membership of the effective slope classes indicates that as the slope increases, the threshold of slope instability also increases and the likelihood of mass movements such as landslides is increased.The north aspects play a more effective role in generating landslide motions due to their higher rainfall and moisture content. Zones with marl formations have the highest fuzzy membership among the other lithological units in the region. In terms of vegetation density factor, fuzzy membership value of 1 was obtained for parts of the region with lower vegetation density. The effective factor of the distance to road shows its prominent role in the occurrence of landslides with fuzzy membership values obtained for its different classes; the less than 250 m distance of the road having the most The fuzzy membership value is the lowest fuzzy membership category with distances greater than 1,800 meters. The combination of landslides in the stream network density layer indicates that the highest landslide distribution is in the low density class (1200 - 75 m / km 2) per unit area. Therefore, zones with loose formations close to the road have greater potential for landslides. As the study area has this discrepancy and the impact of most road and geological factors has overshadowed how other factors are affected.The zoning map with 0.7 gamma shows that the area of very high risk zone between Tuskestan forest to Gorgan is 0.47, high risk zone 0.82, medium risk zone 1.65 and relatively low classes risk and very low risk are 2.54 and 8.95 square kilometers, respectively. the landslide hazard zonation map with 0.8 gamma, the areas of very high, high, medium, relatively low and very low risk areas were 1.05, 1.74, 2.56, 4.43 and 4, 63 km2, respectively. The area of these zones in the zoning map with 0.9 gamma are 1.19, 1.94, 2.49, 4.62 and 4.18 km2, respectively. The value of Quality sum (Qs), which compares and evaluates the methods compared to each other, indicates that the 0.7 with 2.42 fuzzy gamma operator has the highest Qs among the other gamma operators. Therefore, this operator is introduced as the optimal operator in landslide hazard zonation of the study area.

The results show that zones with loose formations, close to the roads and zones with higher rainfall have more potential for landslides. Also, the results of the modeling using selected methods showed different accuracy of them in preparing the final map of landslide zoning in the study area. But Fuzzy Gamma Operator with Landau 0.7 has better utility in landslide zoning than other methods presented. Therefore, any planning and management of the environment should be done according to the results of this model.