This page has only limited features, please log in for full access.
On 18 January 2016, the Zhangjiazhuang high-speed railway tunnel in Ledu, Qinghai Province, China, underwent serious deformation and structural damage. A crack formed at the top of the tunnel and the concrete on the crown peeled off. As a result, the tunnel could not be operated for three months. In order to determine the types and spatial distribution of the landslides in the region and the surface deformation characteristics associated with the tunnel deformation, we used field geological and geomorphological surveys, unmanned aerial vehicle image interpretation and differential interferometric synthetic aperture radar (D-InSAR) surface deformation monitoring. Nine ancient and old landslides were identified and analysed in the study area. Surface deformation monitoring and investigation of buildings in several villages on the slope front showed that the tunnel deformation was not related to deep-seated gravitational slope deformation. However, surface deformation monitoring revealed an active NEE-SWW fault in the area intersecting the tunnel at the location of the tunnel rupture. This constitutes a plausible mechanism for the deformation of the tunnel. Our study highlights the need for detailed engineering geomorphological investigations to better predict the occurrence of tunnel deformation events in the future.
Xing-Min Meng; Tian-Jun Qi; Yan Zhao; Tom Dijkstra; Wei Shi; Yin-Fei Luo; Yuan-Zhao Wu; Xiao-Jun Su; Fu-Meng Zhao; Jin-Hui Ma; Yi Zhang; Guan Chen; Dong-Xia Chen; Mao-Sheng Zhang. Deformation of the Zhangjiazhuang high-speed railway tunnel: an analysis of causal mechanisms using geomorphological surveys and D-InSAR monitoring. Journal of Mountain Science 2021, 18, 1920 -1936.
AMA StyleXing-Min Meng, Tian-Jun Qi, Yan Zhao, Tom Dijkstra, Wei Shi, Yin-Fei Luo, Yuan-Zhao Wu, Xiao-Jun Su, Fu-Meng Zhao, Jin-Hui Ma, Yi Zhang, Guan Chen, Dong-Xia Chen, Mao-Sheng Zhang. Deformation of the Zhangjiazhuang high-speed railway tunnel: an analysis of causal mechanisms using geomorphological surveys and D-InSAR monitoring. Journal of Mountain Science. 2021; 18 (7):1920-1936.
Chicago/Turabian StyleXing-Min Meng; Tian-Jun Qi; Yan Zhao; Tom Dijkstra; Wei Shi; Yin-Fei Luo; Yuan-Zhao Wu; Xiao-Jun Su; Fu-Meng Zhao; Jin-Hui Ma; Yi Zhang; Guan Chen; Dong-Xia Chen; Mao-Sheng Zhang. 2021. "Deformation of the Zhangjiazhuang high-speed railway tunnel: an analysis of causal mechanisms using geomorphological surveys and D-InSAR monitoring." Journal of Mountain Science 18, no. 7: 1920-1936.
Groups of landslides induced by heavy rainfall are widely distributed on a global basis and they usually result in major losses of human life and economic damage. However, compared with landslides induced by earthquakes, inventories of landslides induced by heavy rainfall are much less common. In this study we used high-precision remote sensing images before and after continuous heavy rainfall in southern Tianshui, China, from 20 June to 25 July 2013, to produce an inventory of 14,397 shallow landslides. Based on the results of landslide inventory, we utilized machine learning and the geographic information system (GIS) to map landslide susceptibility in this area and evaluated the relative weight of various factors affecting landslide development. First, 18 variables related to geomorphic conditions, slope material, geological conditions, and human activities were selected through collinearity analysis; second, 21 selected machine learning models were trained and optimized in the Python environment to evaluate the susceptibility of landslides. The results showed that the ExtraTrees model was the most effective for landslide susceptibility assessment, with an accuracy of 0.91. This predictive ability means that our landslide susceptibility results can be used in the implementation of landslide prevention and mitigation measures in the region. Analysis of the importance of the factors showed that the contribution of slope aspect (SA) was significantly higher than that of the other factors, followed by planar curvature (PLC), distance to river (DR), distance to fault (DTF), normalized difference vehicle index (NDVI), distance to road (DTR), and other factors. We conclude that factors related to geomorphic conditions are principally responsible for controlling landslide susceptibility in the study area.
Tianjun Qi; Yan Zhao; Xingmin Meng; Guan Chen; Tom Dijkstra. AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China. Remote Sensing 2021, 13, 1819 .
AMA StyleTianjun Qi, Yan Zhao, Xingmin Meng, Guan Chen, Tom Dijkstra. AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China. Remote Sensing. 2021; 13 (9):1819.
Chicago/Turabian StyleTianjun Qi; Yan Zhao; Xingmin Meng; Guan Chen; Tom Dijkstra. 2021. "AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China." Remote Sensing 13, no. 9: 1819.
The NW-SE-striking fault zone in the Bailong River Basin in the northeastern margin of the Qinghai-Tibet Plateau have the most densely distributed, large complex landslides in China. However, the failure of large complex landslides along the fault zone and the causes of their complex geomorphic characteristics are unclear. The purpose of this study was to explore the relationship between the fault zone and the spatial distribution, direction of movement, and geomorphic characteristics of large landslides. We inventoried 29 large landslides in the middle reaches of the Bailong River and described their characteristics. Statistical analysis revealed differences in the spatial relationship between the faults and landslides of different scales. Almost all of the landslide bodies with an area > 1 km2 are distributed on the faults; in addition, the strike of the faults was found to constrain the direction of movement of the landslides to the WNW-ESE direction. Statistical results show that the cross sections of landslides in the study area are asymmetric arc, corresponding to which there are significant differences in the geomorphological characteristics of the north and south sides of landslides. The geometric characteristics, physical properties (i.e., material weakness) of the fault zone and rapid uplift of the hanging wall were responsible for the asymmetric shape of landslide cross sections and the geomorphic difference. We present a conceptual model based on the relationship between the fault zone and landslides, which facilitates an improved understanding of the relationship between the fault zone and landslide evolution.
Tianjun Qi; Xingmin Meng; Feng Qing; Yan Zhao; Wei Shi; Guan Chen; Yi Zhang; Yajun Li; Dongxia Yue; Xiaojun Su; Fuyun Guo; Runqiang Zeng; Tom Dijkstra. Distribution and characteristics of large landslides in a fault zone: A case study of the NE Qinghai-Tibet Plateau. Geomorphology 2021, 379, 107592 .
AMA StyleTianjun Qi, Xingmin Meng, Feng Qing, Yan Zhao, Wei Shi, Guan Chen, Yi Zhang, Yajun Li, Dongxia Yue, Xiaojun Su, Fuyun Guo, Runqiang Zeng, Tom Dijkstra. Distribution and characteristics of large landslides in a fault zone: A case study of the NE Qinghai-Tibet Plateau. Geomorphology. 2021; 379 ():107592.
Chicago/Turabian StyleTianjun Qi; Xingmin Meng; Feng Qing; Yan Zhao; Wei Shi; Guan Chen; Yi Zhang; Yajun Li; Dongxia Yue; Xiaojun Su; Fuyun Guo; Runqiang Zeng; Tom Dijkstra. 2021. "Distribution and characteristics of large landslides in a fault zone: A case study of the NE Qinghai-Tibet Plateau." Geomorphology 379, no. : 107592.
Debris flows caused by channel bed erosion present major hazards affecting life, livelihoods, and the built environment in mountainous regions. An efficient way to decrease hazard impact is through reliable hazard forecasts and appropriate early-warning strategies. Rainfall thresholds are fundamental in achieving reliable hazard forecasts. However, a lack of rainfall records often impedes the empirical establishment of such thresholds. This paper constructs rainfall intensity-duration thresholds based on process-based critical runoff discharge for the initiation of debris flows and a mathematical approximation among peak discharge, rainfall intensity and duration. Simulations of conditions that triggered debris flow and non-debris flow events allowed determination of the lower and upper limits of critical discharge for debris flow initiation. In turn, these critical discharge limits are compared with four estimates derived from process-based approaches to test which approach best delimit the critical conditions. Hydrological simulations derive S-hydrographs for recorded rainfall events. Further analysis of the S-hydrographs results in a mathematical approximation of peak discharge as a function of rainfall intensity and duration and the establishment of the minimum rainfall required to produce a particular peak discharge. The minimum rainfall threshold to trigger an event can be calculated by setting the process-based critical discharge as the peak dis charge. In turn, this enables the establishment of a conventional rainfall I-D threshold for debris flow initiation. This process-based approach enables the construction of valley-specific I-D thresholds in data-poor areas and provides a promising pathway to improve the reliability of debris flow hazard forecasts and early warnings.
Yajun Li; Xingmin Meng; Peng Guo; Tom Dijkstra; Yan Zhao; Guan Chen; Dongxia Yue. Constructing rainfall thresholds for debris flow initiation based on critical discharge and S-hydrograph. Engineering Geology 2020, 280, 105962 .
AMA StyleYajun Li, Xingmin Meng, Peng Guo, Tom Dijkstra, Yan Zhao, Guan Chen, Dongxia Yue. Constructing rainfall thresholds for debris flow initiation based on critical discharge and S-hydrograph. Engineering Geology. 2020; 280 ():105962.
Chicago/Turabian StyleYajun Li; Xingmin Meng; Peng Guo; Tom Dijkstra; Yan Zhao; Guan Chen; Dongxia Yue. 2020. "Constructing rainfall thresholds for debris flow initiation based on critical discharge and S-hydrograph." Engineering Geology 280, no. : 105962.
The China–Pakistan Karakoram Highway is an important land route from China to South Asia and the Middle East via Pakistan. Due to the extremely hazardous geological environment around the highway, landslides, debris flows, collapses, and subsidence are frequent. Among them, debris flows are one of the most serious geological hazards on the Karakoram Highway, and they often cause interruptions to traffic and casualties. Therefore, the development of debris flow susceptibility mapping along the highway can potentially facilitate its safe operation. In this study, we used remote sensing, GIS, and machine learning techniques to map debris flow susceptibility along the Karakoram Highway in areas where observation data are scarce and difficult to obtain by field survey. First, the distribution of 544 catchments which are prone to debris flow were identified through visual interpretation of remote sensing images. The factors influencing debris flow susceptibility were then analyzed, and a total of 17 parameters related to geomorphology, soil materials, and triggering conditions were selected. Model training was based on multiple common machine learning methods, including Ensemble Methods, Gaussian Processes, Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines, Trees, Discriminant Analysis, and eXtreme Gradient Boosting. Support Vector Classification (SVC) was chosen as the final model after evaluation; its accuracy (ACC) was 0.91, and the area under the ROC curve (AUC) was 0.96. Among the factors involved in SVC, the Melton Ratio (MR) was the most important, followed by drainage density (DD), Hypsometric Integral (HI), and average slope (AS), indicating that geomorphic conditions play an important role in predicting debris flow susceptibility in the study area. SVC was used to map debris flow susceptibility in the study area, and the results will potentially facilitate the safe operation of the highway.
Feng Qing; Yan Zhao; Xingmin Meng; Xiaojun Su; Tianjun Qi; Dongxia Yue. Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway. Remote Sensing 2020, 12, 2933 .
AMA StyleFeng Qing, Yan Zhao, Xingmin Meng, Xiaojun Su, Tianjun Qi, Dongxia Yue. Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway. Remote Sensing. 2020; 12 (18):2933.
Chicago/Turabian StyleFeng Qing; Yan Zhao; Xingmin Meng; Xiaojun Su; Tianjun Qi; Dongxia Yue. 2020. "Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway." Remote Sensing 12, no. 18: 2933.
Debris flow is a major geohazard in mountainous regions and poses a significant threat to life and property. The damage caused by debris flows have increased with the expansion of human settlements and activity into the mountainous regions of China. In regards to risks from debris flows, previously unrecognized low-frequency debris flow catchments constitute an especially significant threat. According to our investigation, only about 500 catchments have debris flow records in >2000 catchments of Bailong River basin. The main purpose of this paper is to introduce a new methodology using Artificial Intelligence (AI) that can simultaneously input parameters related to geomorphological conditions and material conditions to better distinguish low-frequency debris flow catchments (LFDs) from medium-high frequency debris flow catchments (MHFDs). A total of 449 prototypical debris flow catchments, 15 parameters, and 9 commonly used learning machines were used to build identification models. Debris flow catchments are divided into 4 cases (LO1-LO4) based on different sample ratios of LFDs and MHFDs, which are input into each classifier one by one. Based on model evaluation, the CHAID model in the case LO2 performs best, which only uses five parameters (formation lithology index, land use index, vegetation coverage index, drainage density and landslide density index) to predict LFDs. The results indicate that LFDs are mainly distributed in areas with less landslide distribution and better vegetation coverage compared with MHFDs. However, the distribution of LFDs is concentrated on FLI (formation lithology index) =4, which is the weak lithology area. The tree classifier seems to be better at classifying fluvial processes. The model developed in this paper can help us quickly find LFDs in similar areas, and help to assess the risk of debris flows.
Yan Zhao; Xingmin Meng; Tianjun Qi; Feng Qing; Muqi Xiong; Yajun Li; Peng Guo; Guan Chen. AI-based identification of low-frequency debris flow catchments in the Bailong River basin, China. Geomorphology 2020, 359, 107125 .
AMA StyleYan Zhao, Xingmin Meng, Tianjun Qi, Feng Qing, Muqi Xiong, Yajun Li, Peng Guo, Guan Chen. AI-based identification of low-frequency debris flow catchments in the Bailong River basin, China. Geomorphology. 2020; 359 ():107125.
Chicago/Turabian StyleYan Zhao; Xingmin Meng; Tianjun Qi; Feng Qing; Muqi Xiong; Yajun Li; Peng Guo; Guan Chen. 2020. "AI-based identification of low-frequency debris flow catchments in the Bailong River basin, China." Geomorphology 359, no. : 107125.
Debris flows represent major hazards in most mountainous regions of the world where they repeatedly result in disasters. In order to protect people and infrastructure against future debris flows, many debris flow catchments have been artificially intervened by employing various mitigation measures, including civil engineering works. However, the commonly adapted engineering measures, such as check dams, are not effective for every debris flow catchment, and the failure of such measures even causes more damage, e.g. the Sanyanyu debris flow catchment in Zhouqu, China, killed 1756 people. In order to research the effectiveness of engineering strategies and explore much more effective mitigation works for debris flows in the mountainous regions, we took the Bailong River catchment of Southern Gansu of China as study area, with special emphasis on Sanyanyu debris flow catchment (with civil engineering works) and Goulinping debris flow catchment (without civil engineering works), and comparatively analysed the two catchments. The comparative results show that both catchments have similar material source, geomorphological/environmental and climatic conditions, however, vegetation cover and rock hardness are poorer in Goulinping than in Sanyanyu, the catchment that underwent larger-scale debris flows, suggesting that the mitigation measures had been applied in Sanyanyu catchment were inappropriate. Subsequently, we simulated the effectiveness of controlling debris flow peak discharge with check dams at the lower part of Sanyanyu and Goulinping catchment using the Kanako simulator, and summarised argument based on the hypothesis and facts from positive and negative aspects. We draw the conclusion that it is not reasonable to build check dams in the two catchments and instead, drainage channels should be primarily considered for reducing debris flow hazards in such densely populated areas. Finally, we undertook detailed field investigations and experiments on the native plants in the region, and found that the ecological mitigation measure with planting Robinia Pseudoacacia on the debris flow deposits is an effective method to alleviate debris flow hazards. It is concluded that channel works combined with ecological measures are the preferable approaches to minimize the debris flow damage in debris flow catchments characterised with high mountains, concentrated rainfalls and active neotectonic movement.
Muqi Xiong; Xingmin Meng; Siyuan Wang; Peng Guo; Yajun Li; Guan Chen; Feng Qing; Zhijie Cui; Yan Zhao. Effectiveness of debris flow mitigation strategies in mountainous regions. Progress in Physical Geography: Earth and Environment 2016, 40, 768 -793.
AMA StyleMuqi Xiong, Xingmin Meng, Siyuan Wang, Peng Guo, Yajun Li, Guan Chen, Feng Qing, Zhijie Cui, Yan Zhao. Effectiveness of debris flow mitigation strategies in mountainous regions. Progress in Physical Geography: Earth and Environment. 2016; 40 (6):768-793.
Chicago/Turabian StyleMuqi Xiong; Xingmin Meng; Siyuan Wang; Peng Guo; Yajun Li; Guan Chen; Feng Qing; Zhijie Cui; Yan Zhao. 2016. "Effectiveness of debris flow mitigation strategies in mountainous regions." Progress in Physical Geography: Earth and Environment 40, no. 6: 768-793.