Evaluation of Fuzzy Logic and Network Analysis Models for Mapping Landslide Sensitivity Case Study: (Sarab - Nir Road)

Abstract

Introduction Landslide is a term that encompasses a variety of amplitude motions and causes the movement of a mass of material in the slopes. And creep is classified. Natural slope instability is one of the geomorphological and geological phenomena that plays an effective role in deforming the earth's surface. Identifying areas with potential for landslides and their zoning is one of the key steps in managing environmental hazards and reducing the damage caused by this phenomenon, because this phenomenon causes financial and human costs, soil and land degradation and increased sediment production at the basin outlet. It becomes. Iran with its predominantly mountainous topography, high tectonic activity and seismicity, diverse geological and climatic conditions, has the most natural conditions to create a wide range of landslides. The purpose of this study is to zoning the risk of landslides on the Sarab-Nir road. In this research, two models of network analysis and fuzzy logic are examined and evaluated. It is hoped that eventually, by preparing a landslide risk zoning map, it will be of great help to planners and managers in order to reduce potential damages and find safer locations for development, construction and road construction. Methodology Sarab-Nir road is located between East Azarbaijan province and Ardabil province and is a communication route between these two provinces, whose geographical coordinates are 37 degrees and 94 minutes to 38 degrees and 03 minutes north latitude and 47 degrees and 53 minutes to 48 degrees and 01 minutes. It is east longitude. In this study, network analysis model was used to determine areas prone to fall and zoning. In order to better understand the causes of landslides and also to organize the research in the field, the study area was visited and 15 geographical points from different areas of the study area were recorded. The geographical location of the points prone to fall was also recorded with GPS. Then, according to the network analysis model, information layers were prepared in ArcGIS software. The information layers for landslide risk zoning are: fault, slope, slope direction, distance from road, and distance from waterway, land use, geology (lithology), precipitation, altitude and vegetation. The elevation file or digital model of the elevation of the area was prepared with an accuracy of 30 meters from the USGS site and the desired DEM is a digital file obtained from the AST‌‌ER sensor and according to this DEM, the information layer such as streams, slope and direction ‌The slope was obtained. Results and Discussion Four maps have been developed to investigate landslide hazards, which are rainfall, slope, elevation and land use layers. After creating information layers in order to prepare the final landslide hazard map, fuzzy information layer maps were created. In this study, in order to determine the effect of different classes of criteria on landslide sensitivity zoning, the layers are based on the type of performance of each in the landslide event using fuzzy membership functions in the range of zero to 1 fuzzy. Were made. The results obtained from the information layers and finally the landslide hazard map show that altitudes of more than 2000 meters have the highest share of landslides, and altitudes of 1400 meters have been significant landslides due to the instability of the slopes against Climatic and environmental factors. Also, most of the landslides occurred at a distance of 3 to 6 km from the faults, which shows the importance of faults against landslides. About 40% of landslides occur in very high-risk classes. This indicates that the model has a high capability in predicting landslides. It is necessary to explain that most of the landslides occur in the area of Saein pass, which have very favourable conditions for the occurrence of range movements that start from 25 km of mirage and continue for a distance of 15 km of Nir. Conclusion Factors such as slope, precipitation and geology play a more important role in landslides than other factors. Slopes of 60 to 80% have the greatest impact on landslides, which are more pronounced at altitudes above 2000 meters. Therefore, altitudes above 2000 meters have the most landslides. Also, due to the direct relationship between altitude and climatic fluctuations in these altitudes, the amount of precipitation is higher and, of course, has a great impact on the occurrence of landslides. In these areas, vegetation is at a minimum and due to the cold region, the vegetation in these areas is very small, which prepares the conditions for landslides and due to the presence of sedimentary formations such as sandstone, Siltstone mudstone with tuff interbreeds in the area, the conditions for landslides have become more prone and because these formations lose their stability sooner and are strongly influenced by physicochemical factors, they are more prone to landslides than other formations. To be. According to the results, the low risk floor with the highest value, 405.44 square kilometers, occupies approximately 30.87 percent of the area, but the very high risk floor with 288.2 square kilometers and the high risk class with 23.23 square kilometers. , Occupy a total of 37.25% of the area of risk classes.

Keywords

• landslide
• zoning
• fuzzy logic model and network analysis
• gis
• rs