Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors

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.

An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan

“…php). Normalized difference vegetation index (NDVI) (Chen et al 2019a), normalized difference water index (NDWI) (Luo et al 2019), and bare soil index (BI) (Huang et al 2019) were derived from the Landsat TM 8 image data, resampled with a 10 m resolution (Zhu et al 2018). The geological factors were derived from the 1:200,000 scale geological map, which was obtained from the Geological Survey of Japan, AIST (https://www.gsj.jp/en/).…”

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

“…Most previous studies have frequently pointed to the anthropogenic triggering factors such as distance to roads [51,53,78,105,130], road density [76,105,135], land-use/land-cover types [42,47,49,58,78,106], and land-use changes [3,136,137] for the mapping of landslide susceptibility.…”

Predicting Landslides Using Locally Aligned Convolutional Neural Networks

“…Neural networks and convolutional models are among more recent approaches for susceptibility mapping. Luo et al [2019] and Bui et al [2015] use neural networks to assess mine landslide susceptibility and to predict shallow landslide hazards. Wang et al [2019] did a comparative study on CNNs for landslide susceptibility mapping but their approach does not incorporate any orientational information or aligning filters either.…”

“…We use a fully convolutional model for this purpose. These models have been widely used for image segmentation [Shelhamer et al, 2017;Ronneberger et al, 2015;Noh et al, 2015] and usually consist of down-sampling and up-sampling stages. One of the popular models in this category is UNet [Ronneberger et al, 2015], which our architecture is based on.…”

“…Instead, we use interpolation for up-sampling. It has been shown that adding skip connections to a fully convolutional model improves its performance [Drozdzal et al, 2016;Mao et al, 2016]. As short skip connections have been shown to work only in very deep networks, we only apply long connections to our model.…”