Landslide Hazard Analysis Using Frequency Ratio Model

In the north part of Iran (Alborz Mountain belt), landslides occur frequently due to climatologic and geologic conditions and high tectonic activities, that results, annually, millions of dollars financial defect excluding casualties and unrecoverable resources. The reliable hazard map would help to mitigate the consequences of landslide occurrences by land-use management and other strategies. This paper evaluates the hazardous area in Marzan Abad (Central Alborz, North part of Iran) using probabilistic–Frequency ratio (PFR) model, Geographic Information System (GIS) and Remote sensing techniques. Hazardous areas have been analyzed and mapped using the landslide occurrence factors by frequency ratio model.In GIS platform, layers such as geology, geomorphology, soil, slope, aspect, elevation, annual precipitation, land use, distance from faults, lineaments, roads and drainages were displayed, manipulated and analyzed. The validation of hazard map has been estimated with the validation group of actual landslides and rate curves method. The Area Under the Curve (AUC) evaluates how well the method predicts landslides. The results have showen satisfactory agreement between prepared hazard map and existing data on total landslide locations (93.60%) and validation group of landslide locations (91.68%) So, the methodology used in this study was validated. Final hazard map classified in five hazardous classes (very high, high, moderate, low and non hazardous area). Receiver Operating Characteristic curve method (ROC curve) was used to validate the classification and based on its area under the curve value, final classification was evaluated as excellent classification (AUC=0.94). This study evaluates geology, soil and distance to road networks as the most effective factors on landslide analysis and deep valleys, old landslide traces, area near the roads and faults as the most hazardous areas for landslide occurrence in Marzan Abad area

PFR model and GiT for landslide susceptibility mapping: a case study from Central Alborz, Iran

In northern parts of Iran such as the Alborz Mountain belt, frequent landslides occur due to a combination of climate and geologic conditions with high tectonic activities. This results in millions of dollars of financial damages annually excluding casualties and unrecoverable resources. This paper evaluates the landslide susceptible areas in Central Alborz using the probabilistic frequency ratio (PFR) model and Geo-information Technology (GiT). The landslide location map in this study has been generated based on image elements interpreted from IRS satellite data and field observations. The display, manipulation and analysis have been carried out to evaluate layers such as geology, geomorphology, soil, slope, aspect, land use, distance from faults, lineaments, roads and drainages. The validation group of actual landslides and relative operation curve method has been used to increase the accuracy of the final landslide susceptibility map. The area under the curve evaluates how well the method predicts landslides. The results showed a satisfactory agreement of 91% between prepared susceptibility map and existing data on landslide locations

Landslide susceptibility evaluation and factor effect analysis using Probabilistic-Frequency Ratio model

In the North parts of Iran (Alborz Mountain belt), landslides occur frequently due to climatologic and geologic conditions with high tectonic activities. That results, annually, millions of dollars financial defects excluding casualties and unrecoverable resources. In this paper, the landslide susceptibility and the effect of landslide-related factors at Marzan Abad in Iran, using the Probabilistic-Frequency Ratio (PFR) model, geographic information system (GIS) and remote sensing data have been evaluated. Landslide location map has been generated on the basis of image elements interpretation from aerial photos, satellite data and field observations. Display, manipulate and analysis have been carried out to evaluate layers such as geology, geomorphology, slope, soil, land use, distance from roads and drainages. The area under the prediction rate curve, evaluates how well the method predicts landslides. The results showed satisfactory agreement between prepared susceptibility map and existing data on landslide locations (92.59%). To assess the factor effects, each factor was excluded from the analysis and its effect was verified using the landslide location data. It is revealed that all factors have relatively positive effects, on the landslide susceptibility maps in the study. The most effective factor is the lithology and outcrop of the bedrocks (13.7% positive influence) in this area