Volume 22, Issue 3 (Fall 2018)                   JWSS 2018, 22(3): 149-162 | Back to browse issues page

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Nosrati K, Heydari M, Hoseinzadeh M, Emadoddin S. Prediction of Landslide Susceptibility Using Rare Events Logistic Regression (A Case-Study: Ziarat Drainage Basin, Gorgan). JWSS. 2018; 22 (3) :149-162
URL: http://jstnar.iut.ac.ir/article-1-3558-en.html
1. Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran. , k_nosrati@sbu.ac.ir
Abstract:   (4877 Views)
Ziarat drainage basin, in the southern part of Gorgan city, is exposed to mass movement, especially landslide occurrence, due to geologic, geomorphologic, and anthropogenic reasons. The objectives of this study were to predict landslide susceptibility and to analyze the effective factors using rare events logistic regression. In view of this, the map layers of the variables including geology, land use, slope, slope aspect, distance of road, distance of fault and distance of river were prepared using topographic and geologic maps and aerial photo interpretation. In addition, the map layers of the soil variables including the percent of clay, silt, sand, and saturation water as well as plasticity limit index were determined based on the laboratory analysis of 32 soil samples collected from landslide sites and 32 soil samples obtained from non-occurrence landslide sites. The controlling factors of landslide were determined using rare events logistic regression analysis; then based on their coefficients, the landslide risk zoning map was prepared and validated. The landslide risk zoning map was classified in five different hazard classes ranging from very low risk to very high risk; the very high risk class with 16.8 km2 was assigned as the having the highest percent of the catchment area. The results of the model validation showed that the rare events logistic regression model with the receiver operating characteristic (ROC) of 0.69 could be a suitable prediction model for the study area. The results of this study could be, therefore, useful for corrective actions and watershed management landslide high-risk zones.
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Type of Study: Research | Subject: Ggeneral
Received: 2017/08/15 | Accepted: 2017/10/21 | Published: 2018/11/15

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