Abstract
The fragile ecological environment near mines provide advantageous conditions for the development of landslides. Mine landslide susceptibility mapping is of great importance for mine geo-environment control and restoration planning. In this paper, a total of 493 landslides in Shangli County, China were collected through historical landslide inventory. 16 spectral, geomorphic and hydrological predictive factors, mainly derived from Landsat 8 imagery and Global Digital Elevation Model (ASTER GDEM), were prepared initially for landslide susceptibility assessment. Predictive capability of these factors was evaluated by using the value of variance inflation factor and information gain ratio. Three models, namely artificial neural network (ANN), support vector machine (SVM) and information value model (IVM), were applied to assess the mine landslide sensitivity. The receiver operating characteristic curve (ROC) and rank probability score were used to validate and compare the comprehensive predictive capabilities of three models involving uncertainty. Results showed that ANN model achieved higher prediction capability, proving its advantage of solve nonlinear and complex problems. Comparing the estimated landslide susceptibility map with the ground-truth one, the high-prone area tends to be located in the middle area with multiple fault distributions and the steeply sloped hill.
Highlights
Mine landslides are common geological hazards that have caused huge loss of life and property worldwide
Landslide susceptibility modeling (LSM) is considered as a first procedure towards susceptibility assessment, which is a spatial distribution of probabilities of landslide occurrences in a given area based on local geo-environmental factors [2]
The information value model (IVM) model did not perform as well as artificial neural network (ANN) and support vector machine (SVM) referring to AUC values, so it does not participate in the comparison
Summary
Mine landslides are common geological hazards that have caused huge loss of life and property worldwide. Peng et al [9] developed a hybrid model based on the support vector machine (SVM) method to assess landslide susceptibility at the regional scale using multisource data. The main objective of present study is to evaluate and compare the performance of feature selection arithmetic and three assessment methods, including two machine learning models: ANN, SVM and one conventional statistical model: IVM, for mine landslide susceptibility assessment. The uncertainty of the models is analyzed based on the resampling techniques and the rank probability score For this reason, we extract evaluation factors from remote sensing images and spatial data, which are represented by three methods, respectively. The area with high-prone landslide will be identified and the causes will be discussed in this study
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
