Assessment of the landslide effective factors and zonation of this event using logistic regression in the GIS environment: the Taleghan Watershed case study

جغرافیا و مخاطرات محیطی

Article 3, Volume 1, Issue 1, January 2012, Pages 65-82   

Document Type: Research Article

DOI: 10.22067/geo.v1i1.16523

Authors

Shahriyar Khaledi^{* 1}; khohbat Derafshi¹; Akbar Mehrjunejad²; Saeade Gharachahi¹; Shahin khaledi³

¹Shahid Beheshti University

²University of Tehran

³Azad Islamic University, Noor Branch

Abstract

Introduction
A landslide or landslip is a geological phenomenon which includes a wide range of ground movement, such as rock falls, deep failure of slopes and shallow debris flows, which can occur in offshore, coastal and onshore environments. Although the action of gravity is the primary driving force for a landslide to occur, there are other contributing factors affecting the original slope stability. Typically, pre-conditional factors build up specific sub-surface conditions that make the area/slope prone to failure, whereas the actual landslide often requires a trigger before being released.
Landslide is among the natural hazards that causes financial losses and destruction the natural resources. The combination of natural and human factors provides conditions for this unstable range event in the geomorphic transport processes.Landslide hazard is defined as the probability of a landslide at a specific location in space and time. Landslides result from a complex mixture of geologic, geomorphic, and hydrologic conditions causing damage and disruption to people, organizations, industries and the environment. Modification of natural conditions by human activities, such as road building and forest harvesting accelerates land sliding.
Study area
Taleghan watershed with an area equivalent to 1326 km of SefidRoudis the major sub basin in the southern slopes of Alborz Mountain and the northwestern part of Tehran and is located 120 kilometers from the city. Annual rainfall Average and annual temperature of the watershed are 515.16 mm and 10.5 °C respectively. The outlet stream gauging station is named Galinak with an area of 800.5 km2. A second stream gaugeis Joestan Station whichlies in the upper part of the watershed and has an area of 412.7km2.The area is well-known for Iranian people for its mild, sunny summers and cold winters.
Materials and methods
A landslide susceptibility zonation (LSZ) map helps to understand the spatial distribution of slope failure probability in an area and hence it is useful for effective landslide hazard mitigation measures. Such maps can be generated using qualitative or quantitative approaches.

The present study focused on the identity the Taleghan Watershed landslide effective factors and the risk ratio zonation of this event using logistic regression model in the Arc GIS software environment.Landslide zones layer as the dependent variable and geology, distance to fault, distance to road, elevation, slope, aspect, and drainage density layers as the independent variables were entered in the model.
Logistic regression is an approach to prediction,like Ordinary Least Squares (OLS) regression.Logistic regression is typically used when the predictor variables are not normally distributed and some may be categorical. The spatial prediction is modeled by a dependent variable and a number of independent variables that are available in a spatially continuous fashion across the region. Logistic regression is similar to multiple regressions. However, the primary difference is that the dependent variable in the logistic regression is sampled as a binary variable (i.e. presence, absence of landslide). The logistic regression therefore models the probability of presence and absence given observed values of predictor variables.However, with logistic regression, the researcher is predicting a dichotomous outcome. This situation poses problems for the assumptions of (OLS) that the error variances (residuals) are normally distributed.
Result and discussion
Landslide hazard assessment is a primary tool to understand the basic characteristics of the slopes that are prone to landslides especially during extreme rainfall.
In this study, the coefficients obtained from the model indicate the distance to fault variable as the most effective factor on the Watershed landslide events. Based on the layers normalization and the coefficients obtained, zonation of the landslide event risk was done in five categories: very high, high, medium, low and very low. These zones percentage is respectively 28.19, 44.64, 18, 6.86 and 2.33 of the total Watershed area; that 72.83 percentage of the Taleghan Watershed area is located in the high and very high risk categories.
Conclusion
Landslides cause enormous loss of life and property damage in the Taleghan watershed. Assessing landslide related hazard with only limited background information and data is constant challenge for engineers, geologists, planners, landowners, developers, insurance companies, and government entities in the watershed.
Accurate prediction of landslide hazard is difficult because of the complex landslideprocesses and the human activities that constantly reshape the Earth’s surface. Thispaper illustrates the application of a logistic regression modeling technique for modeling landslide probability thata detailed landslide susceptibility map was produced using a logistic regression method with datasets developed for a geographic information system (GIS).This model is careless about the distribution pattern of the independent variables. Most of the topographic factors don’t have normal distributions. Therefore, logistic regression model could get better results comparing other mathematic models.
The results of the model was obtained best fitting function to describe the relationship between influencing factors (independent variables) with a landslide event (dependent variable) and the zoning in study area. The effect of each independent variable on the landslide was assessedfrom the same coefficient in the logistic regression function.

Keywords

Landslide; zonation; Logistic Regression; GIS; Taleghan Watershed

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