COMPARATIVE APPLICATION OF LOGISTIC REGRESSION AND INFORMATION VALUE METHODS IN LANDSLIDE SUSCEPTIBILITY MAPPING, CASE STUDY A PART OF UTTARKASHI DISTRICT (INDIA)

سال انتشار: 1392
محل انتشار: دومین کنفرانس بین المللی مخاطرات محیطی
کد COI اختصاصی: ICEHH02_343
زبان مقاله: انگلیسی
تعداد مشاهده: 924

نویسندگان

Ph.D in geomorphology from Department of Geography, Banaras Hindu University, Varanasi, India

چکیده

The term landslide basically means a slow to rapid downward movement of instable rock and debris masses under the action of gravity. Landslides are one of the major natural hazards that account for hundreds of lives besides enormous damage to properties and blocking the communication links every year. The area chosen for the study is along tow side of the Bhagirathi river valley in Uttarkashi district of Uttarakhand, suffering from frequent landslides every year.One of the enormous occurrences landslide in the study area is Varnavat pravat landslide in uttarkashi city and also and the most affected villages by landslide are Maneri, Mala and Bhatwari. The populations living in these townships and villages suffer badly from the onslaught of landslide. Therefore, landslide susceptibility mapping is one of the important issues for urban and rural planning in India.In this study, layers are evaluated with the help of stability studies used to produce landslide susceptibility map by Logistic Regression and Information Value methods. The parameters of slope, aspect, lithology, land cover, rainfall, distance from fault, distance from river, and distance from road were used as variables in the Logistic Regression analysis and as parameters in Information Value method. ILWIS 3.31 Academic, Arc GIS 9.3, Global Mapper 14.0 and Excel softwares have been used for zonation, and statistical analyses respectively. Finally, an overlay analysis was carried out by evaluating the layers obtained according to their weighting in final Information value method and accepted coefficient in final Logistic Regression model. Then to verification of models and selection optimum model ROC test is used. On the basis of ROC verification method, the logistic regression has the best landslide susceptibility zonation with prediction accuracy of 83.20 percent and is the optimum model for Uttarkashi area and the information Value method has the lower accuracy then Logistic regression method about of 81.40percent, with a difference of about 1.8percent. The study area has been classified into five classes of relative landslide susceptibility, namely, Very Low, Low, Moderate, High, and Very High.

کلیدواژه ها

India, Uttarkashi, Landslide Susceptibility Zonation, Logistic Regression method, Information Value Method, ROC

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