بررسی کارایی روش های دو و چند متغیره در پهنه بندی خطر زمین لغزش (مطالعه موردی: حوضه چهل چای)

به منظور مدیریت و کاهش خطرات ناشی از وقوع زمین لغزش نیاز است تا مناطق مختلف از نظر این خطر، پهنه بندی شود؛ بدین منظور در این تحقیق با استفاده از روش های شبکه عصبی مصنوعی، رگرسیون لجستیک، نسبت فراوانی، شاخص آماری و دمپستر شفر، به پهنه بندی خطر زمین لغزش در حوضه ی چهل چای استان گلستان پرداخته شد. پس از تهیه ی نقشه ی پراکنش زمین لغزش ها، نقشه ی فاکتورهای مستقل موثر در وقوع زمین لغزش شامل شیب، جهت شیب، فاصله از جاده، فاصله از رودخانه، کاربری اراضی، انحنای کل، انحنای دشت، انحنای پروفیل، ارتفاع، شاخص رطوبت توپوگرافیکی، زمین شناسی و فاصله از گسل تهیه شد. برای آموزش و آزمون مدل های مختلف، 91 زمین لغزش مشاهداتی به دو گروه تقسیم بندی شد: آموزش و تست. آموزش، شامل 80 درصد کل زمین لغزش ها (73 زمین لغزش) و تست، شامل 20 درصد زمین لغزش ها (18 زمین لغزش) است. نتایج نشان داد، مقدار مساحت حاصل از زیر منحنی roc داده های تست برای روش های شبکه عصبی (0/86)، رگرسیون لجستیک (0/77)، دمپستر شفر (0/77)، نسبت فراوانی (0/72) و شاخص آماری (0/71) است. به طور کلی هم از نظر مساحت زیر منحنی roc و هم از نظر تعداد زمین لغزش های مشاهداتی در کلاس های مختلف حساسیت، بهترین عملکرد مربوط به روش های چند متغیره ی شبکه عصبی مصنوعی و رگرسیون لجستیک بود و در بین روش های دو متغیره نیز روش دمپستر شفر، عملکرد بهتری نسبت به سایر روش های دو متغیره داشت.

Comparing Bivariate and Multivariate Methods in Landslide Sustainability Mapping: A Case Study of Chelchay Watershed

1 INTRODUCTIONIn the last decades, due to human interventions and the effect of natural factors, the occurrence of landslide increased especially in the north of Iran, where the amount of rainfall is suitable for the landslide occurrence. In order to manage and mitigate the damages caused by landslide, the potential landslideprone areas should be identified.In landslide susceptibility mapping, using the independent conditioning factors, the probability of the spatial occurrence of landslide in an area is estimated (1, 2). There are different qualitative and quantitative approaches to prepared landslide sustainability maps. Quantitative approaches can be divided into three categories: Statistical, probabilistic and distributionfree methods (3). Statistical methods include bivariate and multivariate methods. In bivarite statistical methods, each individual thematic data layer is crossed with the landslide inventory maps, and the weight values, indicating the importance of each parameter class in the landslide occurrence, are assigned to each factor class (4). In contrast, in multivariate methods, the relative contribution of each conditioning factor to landslide occurrence is calculated (5). Each method of mapping has advantages and disadvantages, and there is no one method accepted universally for the effective assessment of landslide hazards.2 METHODOLOGYIn this study, artificial neural network, Logistic regression, frequency ratios, statistical index and Dempster ndash;Shafer methods were used for landslide susceptibility mapping in Chehel Chay watershed in Golestan province. This watershed covers an area of about 256.83 km2 between longitude 36 deg;59 prime; and 37 deg;13 prime;E and between the latitude 55 deg; 23 prime; and 55 deg; 38 prime; N, with the elevation ranging from 179.3 in the northern part to over 2928.3 in the southern part. The mean annual precipitation is 766.5 mm and the dominant land use in this watershed is forest.The first step in the landslide susceptibility assessment is mapping the existing landslides. In this study, using air photograph, as in previous studies, Google Earth and field surveys landslide inventory map were constructed. As landslides inventory maps constructed, using geology, topographic and land use maps thematic layers of 12 landslide conditioning factors including slope angle, slope aspect, curvature, profile curvature, plan curvature, altitude, distance from roads, distance from rivers, lithology, distance from faults, land use and topographic wetness index were prepared. To train and validate different methods, the landslide inventory was randomly split into a training dataset of 80% (73 landslide locations), for estimating the artificial neural network and logistic regression parameters and bivariate models weights, and a testing dataset of 20% (18 landslides locations). By translating bivariate methods weights to thematic layers and implementing the artificial neural network and logistic regression to all the study area, pixels landslide sustainability maps were prepared. Additionally, to evaluate landslide susceptibility maps areas under the ROC curve, the percentage of observed test landslide in each landslide susceptibility class and the area of very high susceptibility class were used.3 RESULTS Results showed that the area under the prediction curve for artificial neural network, logistic regression, Dempster ndash;Shafer, frequency ratio and statistical index were 0.86, 0.77, 0.77, 0.72, and 0.71, respectively. Frequency ratio, artificial neural network, Logistic regression, statistical index, and Dempster ndash;Shafer had the least area of very high susceptibility class, respectively. The percentage of landslide pixels coincided with the sites falling in the very high susceptibility classes for Dempster ndash;Shafer, Artificial neural network, Logistic regression, statistical index and frequency ratio, were 0.72, 0.52, 0.32, 0.22, 0.09 respectively. With respect to the area under prediction curve, the percentage of landslide pixels coincided with the sites falling in the very high susceptibility class; multivariate methods including artificial neural network and logistic regression outperformed the other bivariate methods; also Dempster ndash;Shafer had better performance than the other bivariate models. A similar result was obtained by Kavzoglu et al. (2015) and Pradhan and Lee (2010). On the contrary, Ozdemir and Altural (2013), Lee and Pradhan (2007) and Park (2011) concluded that bivariate models had better performance than multivariate methods. Using forward logistic regression, the factors of slope angle, plan curvature, elevation, distance from roads, distance from rivers, lithology and land use were selected as the most important factors. As distance from the road, fault and river increased, the occurrence of landslide and the weight of bivarite methods decreased.4 CONCLUSIONS SUGGESTIONSIn this study, the capability of bivarite and multivariate methods in landslide sustainability mapping in ChelChay watershed was evaluated. Results showed that with respect to the area under the prediction curve, the percentage of landslide pixels coincided with the sites falling in the very susceptibility class, and regarding the area of very high susceptibility class, multivariate methods had better performance.