@article{ref1,
title="Quantitative assessment of landslide susceptibility comparing statistical index, index of entropy, and weights of evidence in the Shangnan Area, China",
journal="Entropy (Basel, Switzerland)",
year="2018",
author="Liu, Jie and Duan, Zhao",
volume="20",
number="11",
pages="e868-e868",
abstract="In this study, a comparative analysis of the statistical index (SI), index of  entropy (IOE) and weights of evidence (WOE) models was introduced to landslide  susceptibility mapping, and the performance of the three models was validated and  systematically compared. As one of the most landslide-prone areas in Shaanxi  Province, China, Shangnan County was selected as the study area. Firstly, a series  of reports, remote sensing images and geological maps were collected, and field  surveys were carried out to prepare a landslide inventory map. A total of 348  landslides were identified in study area, and they were reclassified as a training  dataset (70% = 244 landslides) and testing dataset (30% = 104 landslides) by random  selection. Thirteen conditioning factors were then employed. Corresponding thematic  data layers and landslide susceptibility maps were generated based on ArcGIS  software. Finally, the area under the curve (AUC) values were calculated for the  training dataset and the testing dataset in order to validate and compare the  performance of the three models. For the training dataset, the AUC plots showed that  the WOE model had the highest accuracy rate of 76.05%, followed by the SI model  (74.67%) and the IOE model (71.12%). In the case of the testing dataset, the  prediction accuracy rates for the SI, IOE and WOE models were 73.75%, 63.89%, and  75.10%, respectively. It can be concluded that the WOE model had the best prediction  capacity for landslide susceptibility mapping in Shangnan County. The landslide  susceptibility map produced by the WOE model had a profound geological and  engineering significance in terms of landslide hazard prevention and control in the  study area and other similar areas.<p /> <p>Language: en</p>",
language="en",
issn="1099-4300",
doi="10.3390/e20110868",
url="http://dx.doi.org/10.3390/e20110868"
}