Landslide susceptibility zonation using GIS and evidential belief function model
- PDF / 6,285,646 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 52 Downloads / 192 Views
ORIGINAL PAPER
Landslide susceptibility zonation using GIS and evidential belief function model Yanli Wu 1 & Yutian Ke 2
Received: 11 November 2015 / Accepted: 12 October 2016 # Saudi Society for Geosciences 2016
Abstract The main goal of this paper is to generate a landslide susceptibility map through evidential belief function (EBF) model by using Geographic Information System (GIS) for Qianyang County, Shaanxi Province, China. At first, a detailed landslide inventory map was prepared, and the following ten landslide-conditioning factors were collected: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, distance to rivers, geomorphology, lithology, and rainfall. The landslides were detected from the interpretation of aerial photographs and supported by field surveys. A total of 81 landslides were randomly split into the following two parts: the training dataset 70 % (56 landslides) were used for establishing the model and the remaining 30 % (25 landslides) were used for the model validation. The ArcGIS was used to analyze landslide-conditioning factors and evaluate landslide susceptibility; as a result, a landslide susceptibility map was generated by using EBF and ArcGIS 10.0, thus divided into the following five susceptibility classes: very low, low, moderate, high, and very high. Finally, when we validated the accuracy of the landslide susceptibility map, both the success-rate and prediction-rate curve methods were applied. The results reveal that a final susceptibility map has the success rate of 83.31 % and the prediction rate of 79.41 %. Keywords Landslide . Zonation . GIS . Evidential belief function (EBF) . China * Yanli Wu [email protected]
1
School of Resources and Geoscience, China University of Mining and Technology, Xuzhou 221116, China
2
School of Civil Engineering and Mechanics, Lanzhou University, Lanzhou 730000, China
Introduction Landslide is one of the most damaging hazards in the world that usually cause extensive damage to property and occasionally result in loss of life (Guzzetti et al. 1999; Lee et al. 2007). Globally, landslides cause approximately 1000 deaths per year and property damage of about US 4 billion (Lee and Pradhan 2007). Also, landslides as one of the most damaging natural disasters are common in many parts of China. China was the most seriously affected country with 695 landslide-induced deathsintheworldaccording to the International Landslide Centre of the University of Durham recorded in 2007 (Thanh and De 2012). More than 20,000 hazards associated with landslides occurred in 2013 and 2014. About 1000 casualties were caused, and property damages amounted to an approximate value of US 1.5 billion (http://www. cigem.gov.cn). Especially, for the current study area, thousands of people still lived in the landslide-prone area. However, few attempts are made to prevent damage caused by landslide disasters at present. In order to solve this problem, it is necessary to map landslide susceptibility zones and predict which areas are susceptible
Data Loading...
