Abstract
The main purpose of this study is to establish an effective landslide susceptibility zoning model and test whether underground mined areas and ground collapse in coal mine areas seriously affect the occurrence of landslides. Taking the Fenxi Coal Mine Area of Shanxi Province in China as the research area, landslide data has been investigated by the Shanxi Geological Environment Monitoring Center; adopting the 5-fold cross-validation method, and through Geostatistics analysis means the datasets of all non-landslides and landslides were divided into 80:20 proportions randomly for training and validating models. A set of 15 condition factors including terrain, geological, hydrological, land cover, and human engineering activity factors (distance to road, distance to mined area, ground collapse density) were selected as the evaluation indices to construct the susceptibility assessment model. Three machine learning algorithms for landslide susceptibility prediction (LSP) including C5.0 Decision Tree (C5.0), Random Forest (RF), and Support Vector Machine (SVM) have been selected and compared through the Areas under the Receiver Operating Characteristics (ROC) Curves (AUC), and several statistical estimates. The study revealed that for these three models the value range of prediction accuracies vary from 83.49 to 99.29% (in the training stage), and 62.26–73.58% (in the validation stage). In the two stages, AUCs are between 0.92 to 0.99 and 0.71 to 0.80 respectively. Using Jenks Natural Breaks algorithm, three LSPs levels are established as very low, low, medium, high, and very high probability of landslide by dividing the indices of the LSP. Compared with RF and SVM, C5.0 is considered better in five categories according to quantities and distribution of the landslides and their area percentage for different LSP zones. Four factors such as distance to road, lithology, profile curvature, and ground collapse density are the most suitable condition factors for LSP. The distance to mine area factor has a medium contribution and plays an obvious role in the occurrence of landslides in all the models. The result reveals that C5.0 possesses better prediction efficiency than RF and SVM, and underground mined area and ground collapse sifnigicantly affect significantly the occurrence of landslides in the Fenxi Coal Mine Area.
Highlights
Mine geological hazards are a kind of man-made geological hazard and caused by geological processes and human engineering activities
In order to reveal whether the Random Forest (RF) and C5.0 is more fit in a coal mine area, and better evaluate the impact of coal mining on landslides, and whether the mining disturbance such as the underground mined area and ground collapses have some contribution to the occurrence of mine geological hazards, this paper takes the Fenxi Coal Mining Area as the research area and uses three machine learning methods: RF, Support Vector Machine (SVM), and C5.0 to model landslide sensitivity
Cross-validation estimation of the predictive performance of a model is a crucial step in predictive modeling, and spatial cross-validation is recommended for spatial data, which may be subject to spatial autocorrelation (Su et al, 2017a), so the 5-fold cross-validation mean is adopted to calculate these landslide susceptibility prediction (LSP) indices
Summary
Mine geological hazards are a kind of man-made geological hazard and caused by geological processes and human engineering activities. Shanxi Province is a famous coalproducing area in China. Due to the overexploitation of coal resources in the area and the special topography of the environment, Shanxi Province has become one of the most developed/mined underground areas leading to frequent ground collapse, which often induced landslides. The geological hazards have the characteristics of wide distribution, significant influence, and prominent potential hazards (Uitto and Shaw, 2016; Su et al, 2020). It is very valuable to recognize and map those areas where landslides have a high probability of occurrence for land use plans and hazard controls (Su et al, 2017a; Huang et al, 2020a), and landslide susceptibility prediction (LSP) can efficiently achieve this purpose (Borrelli et al, 2018; Huang et al, 2021b). An LSP involves some important issues including the extraction of landslide-related environmental factors and the selection of the LSP model (Tien et al, 2015)
Sign-in/Register to view highlights & summary 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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
