A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide suscepti
- PDF / 18,291,227 Bytes
- 16 Pages / 595.276 x 790.866 pts Page_size
- 104 Downloads / 176 Views
ORIGINAL PAPER
A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping Wei Chen 1 & Huichan Chai 2 & Xueyang Sun 1 & Qiqing Wang 2 & Xiao Ding 3 & Haoyuan Hong 4
Received: 14 January 2015 / Accepted: 18 September 2015 # Saudi Society for Geosciences 2016
Abstract The aim of this study is to generate reliable susceptibility maps using frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) models based on geographic information system (GIS) for the Qianyang County of Baoji City, China. At first, landslide locations were identified by earlier reports, aerial photographs, and field surveys, and a total of 81 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70 % (56 landslides) for training the models, and the remaining 30 % (25 landslides) was used for validation purpose. In this case study, 13 landslide-conditioning factors were exploited to detect the most susceptible areas. These factors are slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, distance to faults, distance to rivers, distance to roads, Sediment Transport Index (STI), Stream Power Index (SPI), Topographic Wetness Index (TWI), and lithology. Subsequently, landslide-susceptible areas were mapped using the FR, SI, and WoE models based on landslide-conditioning factors. Finally, the accuracy of the landslide susceptibility maps produced from the three models was verified by using areas under the curve (AUC). The AUC plot estimation results showed that the susceptibility map
* Wei Chen [email protected] 1
School of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
2
School of Resources and Earth Science, China University of Mining and Technology, Xuzhou 221116, China
3
College of Architecture and Civil Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
4
Jiangxi Provincial Meteorological Observatory, Jiangxi Meteorological Bureau, Nanchang 330046, China
using FR model has the highest training accuracy of 83.62 %, followed by the SI model (83.45 %), and the WoE model (82.51 %). Similarly, the AUC plot showed that the prediction accuracy of the three models was 79.40 % for FR model, 79.35 % for SI model, and 78.53 % for WoE model, respectively. According to the validation results of the AUC evaluation, the map produced by FR model exhibits the most satisfactory properties. Keywords Landslide . Statistical model . Areas under the curve (AUC) . Qianyang county . China
Introduction Landslides, resulting in significant damage to people and property, are one of the most costly and damaging geological hazards in many areas of the world. The frequency of landslide occurrences increases with growing human population. Globally, landslides cause hundreds of billions of dollars in damage, thousands of casualties and fatalities, and environmental losses each year (Aleotti and Chowdhury 1999). In
Data Loading...
