Landslide Susceptibility Zoning Using C5.0 Decision Tree, Random Forest, Support Vector Machine and Comparison of Their Performance in a Coal Mine Area

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.

Degrading permafrost river catchments and their impact on Arctic Ocean nearshore processes

Arctic warming is causing ancient perennially frozen ground (permafrost) to thaw, resulting in ground collapse, and reshaping of landscapes. This threatens Arctic peoples' infrastructure, cultural sites, and land-based natural resources. Terrestrial permafrost thaw and ongoing intensification of hydrological cycles also enhance the amount and alter the type of organic carbon (OC) delivered from land to Arctic nearshore environments. These changes may affect coastal processes, food web dynamics and marine resources on which many traditional ways of life rely. Here, we examine how future projected increases in runoff and permafrost thaw from two permafrost-dominated Siberian watersheds—the Kolyma and Lena, may alter carbon turnover rates and OC distributions through river networks. We demonstrate that the unique composition of terrestrial permafrost-derived OC can cause significant increases to aquatic carbon degradation rates (20 to 60% faster rates with 1% permafrost OC). We compile results on aquatic OC degradation and examine how strengthening Arctic hydrological cycles may increase the connectivity between terrestrial landscapes and receiving nearshore ecosystems, with potential ramifications for coastal carbon budgets and ecosystem structure. To address the future challenges Arctic coastal communities will face, we argue that it will become essential to consider how nearshore ecosystems will respond to changing coastal inputs and identify how these may affect the resiliency and availability of essential food resources.

Evaluation of sewer network resilience index under the perspective of ground collapse prevention

Generally, when evaluating the resilience of infrastructure, the four properties of resilience robustness, rapidity, resources, and redundancy (4Rs) are widely considered. However, there is little research on the resilience assessment of sewer networks. Therefore, to establish a framework to evaluate sewer network resilience under the perspective of urban ground collapse prevention, this study considers the 13 second-level detailed indicators corresponding to the 4 first-level indicators (4Rs) based on literature reviews and experts' opinions. An analytic hierarchy process (AHP) is used to obtain relative weights of each indicator and a weighted sum method (WSM) is used to evaluate sewer network resilience index (SRI). The evaluation system was applied to 8 small blocks of selected drainage areas in Seoul, South Korea, and the SRI of 8 small blocks are computed. This study could help the sewer management department to make decisions and manage sewer network assets that enhance the resilience of the sewer networks.

Compound Karst Cave Treatment and Waterproofing Strategy for EPB Shield Tunnelling in Karst Areas: A Case Study

There is high risk of water inrush and ground collapse accidents when tunnelling in karst areas. Based on the case study of an urban metro tunnel, this paper focuses on karst cave treatment and waterproofing strategies for earth pressure balancing (EPB) shield tunnelling in karst areas containing large amounts of karst caves and fissures. When the shield machine enters the karst area, water gush easily occurs, posing serious threats to tunnelling safety. The distribution characteristic of limestone fractures, karst caves, and fissures in the karst area were analyzed according to the geological survey results. Further, water inrush risk and engineering difficulties were analyzed. Subsequently, a compound karst cave treatment and waterproofing strategy for EPB shield tunnelling was proposed and implemented. Water inflow is successfully reduced and ground collapse accident is avoided using the compound karst cave treatment and waterproofing strategy.

Study on Ground Collapse of Covered Karst Soil Caves by Sudden Drop of Groundwater

In this study, the ellipsoidal soil cave with vertical collapses in the covering karst area is studied. Based on certain assumptions, the mechanical model of karst collapse caused by groundwater drop was established. Then, based on the negative pressure calculation formula of soil cave cavity according to Boyle–Mariotte’s law, the expression of the stability coefficient of the soil cave was proposed. Subsequently, the feasibility of the theoretical formula was verified. The calculation example analyzed the relationship of groundwater parameters and overburden thickness. The results show that when the initial groundwater level is higher than the top of cave, the law between the stability coefficient of soil cave and groundwater drawdown shows the jumping horizontal broken line. Thus, soil cave tends to collapse when the falling groundwater level drops over the vault; when the initial groundwater level ranges from the bottom to the top of the cave body, the stability coefficient and groundwater drawdown show a negative correlation law, the curve is steep at the early stage and then becomes gentle at the latter stage, and the higher the initial groundwater level in the cave is, the greater stability coefficient of soil cave reduces; when the initial groundwater level is lower than the bottom of the cave, the effect of drawdown is limited. In addition, for the small drawdown or low initial groundwater level, the stability coefficient of soil cave first decreases and then increases with the increases in thickness of overburden, and the thinner the overburden is, the greater the drawdown rate is; when the drawdown or the initial groundwater level is higher, the stability coefficient of soil cave positively relates to the thickness of the overburden layer.

An autonomous excavator system for material loading tasks

Excavators are widely used for material handling applications in unstructured environments, including mining and construction. Operating excavators in a real-world environment can be challenging due to extreme conditions—such as rock sliding, ground collapse, or excessive dust—and can result in fatalities and injuries. Here, we present an autonomous excavator system (AES) for material loading tasks. Our system can handle different environments and uses an architecture that combines perception and planning. We fuse multimodal perception sensors, including LiDAR and cameras, along with advanced image enhancement, material and texture classification, and object detection algorithms. We also present hierarchical task and motion planning algorithms that combine learning-based techniques with optimization-based methods and are tightly integrated with the perception modules and the controller modules. We have evaluated AES performance on compact and standard excavators in many complex indoor and outdoor scenarios corresponding to material loading into dump trucks, waste material handling, rock capturing, pile removal, and trenching tasks. We demonstrate that our architecture improves the efficiency and autonomously handles different scenarios. AES has been deployed for real-world operations for long periods and can operate robustly in challenging scenarios. AES achieves 24 hours per intervention, i.e., the system can continuously operate for 24 hours without any human intervention. Moreover, the amount of material handled by AES per hour is closely equivalent to an experienced human operator.

STUDY ON THE CHARACTERISTICS OF BEIJING SUBSIDENCE BASED ON PS-INSAR/LEVELING AND PRIMARY INVESTIGATION OF THE RELATIONSHIP WITH FAULT ZONE

The severe land subsidence could lead to ground collapse, building damage and a series of disasters. Up to now, the land subsidence has occurred in more than 50 cities in China, which seriously affects the life and production safety of local people and restricts the development of cities. While, Beijing is one of the most serious cities. This paper takes the urban area of Beijing as an example. PS-InSAR technology is used to process 40 scenes of Terra SAR images from 2010 to 2015, and the high-coherence points are selected by fusing the two algorithms of coherence coefficient and amplitude deviation. In order to verify the reliability of the results, the second-level measurement results are compared with the PS-InSAR deformation results, and five leveling points are used to evaluate the accuracy. The results show that: the maximum absolute error between the Leveling results and the InSAR measurement result is 8.87 mm, and the standard error is 3.22 mm, which meets the accuracy requirements. And areas with serious subsidence occur in Changping District, Haidian District, Daxing District, and Chaoyang District; there is no obvious subsidence trend in the central and eastern parts of Dongcheng, Xicheng and Fengtai District, and the surface is relatively stable. The subsidence in Tongzhou and Shunyi District is serious relatively, the subsidence in these two areas is -6 mm/a~-67 mm/a and -11 mm/a~-22 mm/a respectively. Finally, the spatial relationship between fault zones and land subsidence was preliminarily discussed. The results show that, the subsidence of the south of Changping was so serious than others while the Nankou-Sunhe fault zone crossed with Babaoshan and Huangzhuang-Gaoliying fault zone through this area respectively.