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.
Highlights
Landslides, as one of the most critical geological hazards in the world, seriously threaten lives, property and natural resources [1,2,3,4,5]
The results show that all the Average Merit (AM) values of the conditioning factors were larger than zero, indicating that the thirteen selected factors have positive influence on landslide occurrence
The landslide data were classified into two groups, namely a training dataset (70% of the landslides) and a testing dataset (30% of the landslides)
Summary
Landslides, as one of the most critical geological hazards in the world, seriously threaten lives, property and natural resources [1,2,3,4,5]. Previous studies of landslide susceptibility mapping found that the quality of the data, the depth of the research and the methods of analysis were the three most important factors with a primary effect on the accuracy and reliability of the assessment results [6,9,10,11]. Entropy 2018, 20, 868 apply relevant theories to landslide susceptibility assessment These methods can be categorized into heuristic, deterministic, and statistical approaches [12]. Statistical models can be further categorized into traditional statistical methods, advanced machine learning technologies, and hybrid integration approaches. We address compare three statistical models, applying, analyzing and inspecting the statistical index (SI), index of entropy (IOE), and weights of evidence models (WOE) with regard to landslide susceptibility mapping, using the case study of Shangnan Country, China
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