Basic theory of three-dimensional landslide modeling based on the logical model of geologic structure

Dynamic visualization of landslide cross-sections is important for understanding the structure and mechanism of landslide formation. Moreover, the modeling of geologic information plays an effective role in geo-hazard assessment and their mitigation. In this study, we developed the basic theory of a three-dimensional landslide modeling and applied it to the Nigawa landslide of the Hyogo Prefecture in central Japan. The construction of this model is based on the boundary surfaces of slump blocks and geologic units, and the hierarchical relationships between these surfaces. An application algorithm was validated and the model proved efficient in depicting the nature of landslides in the Nigawa area.     

滑坡前兆突变异常识别方法

将滑坡位移观测值时间序列视为非平稳随机过程, 采用建立系统的同态模型的方法提取短期异常 , 并给出了异常识别准则、有序度计算公式; 用一阶差分进行高通滤波的方法提取临滑异常并进行预测; 根据异常时刻组成的灾变日期集用灰色灾变模型进行预测。     

Field variability of landslide model parameters

 A data set of parameters (slope, soil depth and soil shear strength) relevant to spatially distributed modelling of shallow landslides triggered by rain and snowmelt events was determined from field measurements in 250 grid elements of dimensions 25 m (downslope)×10 m (across slope) in an area of 250 m×250 m on a hillslope in Scotland. These data provide an unusually detailed basis for the evaluation of spatial variability and uncertainty in model parameterisation. The variations in slope and soil strength are represented adequately by normal distributions; a Weibull distribution is suggested for the soil depth data. The factor of safety calculated at each point in the grid was shown partially to identify observed landslides, with a number of false predictions of occurrence. Trend analysis and semivariogram analysis of the data set suggest that the use of kriging could improve upon this approach to landslide prediction by providing areal estimates of parameters at the grid element scale with associated error bounds. Received: 30 October 1996 · Accepted: 25 June 1997     

A review of assessing landslide frequency for hazard zoning purposes

The probability of occurrence is one of the key components of the risk equation. To assess this probability in landslide risk analysis, two different approaches have been traditionally used. In the first one, the occurrence of landslides is obtained by computing the probability of failure of a slope (or the reactivation of existing landslides). In the second one, which is the objective of this paper, the probability is obtained by means of the statistical analysis of past landslide events, specifically by the assessment of the past landslide frequency. In its turn, the temporal frequency of landslides may be determined based on the occurrence of landslides or from the recurrence of the landslide triggering events over a regional extent. Hazard assessment using frequency of landslides, which may be taken either individually or collectively, requires complete records of landslide events, which is difficult in some areas. Its main advantage is that it may be easily implemented for zoning. Frequency assessed from the recurrence of landslide triggers, does not require landslide series but it is necessary to establish reliable relations between the trigger, its magnitude and the occurrence of the landslides. The frequency of the landslide triggers can be directly used for landslide zoning. However, because it does not provide information on the spatial distribution of the potential landslides, it has to be combined with landslide susceptibility (spatial probability analysis) to perform landslide hazard zoning. Both the scale of work and availability of data affect the results of the landslide frequency and restrict the spatial resolution of frequency zoning as well. Magnitude–frequency relationships are fundamental elements for the quantitative assessment of both hazard and risk.     

Analytic network process model for landslide hazard zonation

Various controlling factors such as lithology, slope angle, slope aspect, landuse, channel proximity etc. are generally considered for the landslide hazard assessment. Although outer dependence of these parameters to a landslide is inevitably taken into account, inter-dependence among the factors is seldom addressed. Analytic Network Process (ANP) is the multi-criteria decision making (MCDM) tool which takes into account such a complex relationship among parameters. In this research, an ANP model for landslide susceptibility is proposed, priority weights for each parameter controlling the landslide were determined, and a hazard map was prepared of an area in a fragile mountainous terrain in the eastern part of Nepal. The data used in the example were derived from published sources, aerial photographs and a topographic map. However, the procedures developed can readily incorporate additional information from more detailed investigations.     

National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data

China is one of the countries where landslides caused the most fatalities in the last decades.The threat that landslide disasters pose to people might even be greater in the future,due to climate change and the increasing urbanization of mountainous areas.A reliable national-scale rainfall induced landslide suscep-tibility model is therefore of great relevance in order to identify regions more and less prone to landslid-ing as well as to develop suitable risk mitigating strategies.However,relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area.The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China.In this context,it is aimed to explore the benefit of mixed effects mod-elling to counterbalance associated bias propagations.Six influencing factors including lithology,slope,soil moisture index,mean annual precipitation,land use and geological environment regions were selected based on an initial exploratory data analysis.Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information:Set 1(disregards the presence of incomplete landslide information),Set 2(excludes factors related to the incompleteness of landslide data),Set 3(accounts for factors related to the incompleteness via random effects).The vari-able sets were then introduced in a generalized additive model(GAM:Set 1 and Set 2)and a generalized additive mixed effect model(GAMM:Set 3)to establish three national-scale statistical landslide suscep-tibility models:models 1,2 and 3.The models were evaluated using the area under the receiver operating characteristics curve(AUROC)given by spatially explicit and non-spatial cross-validation.The spatial pre-diction pattern produced by the models were also investigated.The results show that the landslide inven-tory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models.The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However,although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9),it was not associated with the most plausible representation of landslide susceptibility.The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias.The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility.However,a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g.,the Kuenlun Mountains in the northern Tibetan Plateau).The non-linear mixed-effects model(Model 3)reduced the impact of these biases best by introducing bias-describing variables as random effects.Among the three models,Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive perfor-mance(median AUROC of spatial cross validation 0.84)compared to the other two models(median AUROCs of 0.81 and 0.79,respectively).We conclude that ignoring landslide inventory-based incomplete-ness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas.     

A simple definition of a landslide

This study makes a significant progress in addressing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program, an unprecedented disaster mitigation program in China, where lots of newly established monitoring slopes lack sufficient historical deformation data, making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards. A slope displacement prediction method based on transfer learning is therefore proposed. Initially, the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data, thus enabling rapid and efficient predictions for these slopes. Subsequently, as time goes on and monitoring data accumulates, fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy, enabling continuous optimization of prediction results. A case study indicates that, after being trained on a multi-slope integrated dataset, the TCN-Transformer model can efficiently serve as a pre-trained model for displacement prediction at newly established monitoring slopes. The three-day average RMSE is significantly reduced by 34.6% compared to models trained only on individual slope data, and it also successfully predicts the majority of deformation peaks. The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2%, demonstrating a considerable predictive accuracy. In conclusion, taking advantage of transfer learning, the proposed slope displacement prediction method effectively utilizes the available data, which enables the rapid deployment and continual refinement of displacement predictions on newly established monitoring slopes.     

论滑坡地质模型

在综合性滑坡分类体系基础上,把握滑坡活动各要素的地位与作用,遴选最能表征其活动特点的主要因素,作为建模的基本要素,形成滑坡基本地质模型体系;随着勘探阶段的不同,针对不同的应用目的,建立滑坡具体地质模型体系。滑坡地质模型的建立可为其分析、计算、评价与监测预报奠定模式基础,并有助于对滑坡活动的全面抽象掌握,最终提高滑坡工作的科学系统性和实用性。     

A landslide database for Nicaragua: a tool for landslide-hazard management

A digital landslide database has been created for Nicaragua to provide the scientific community and national authorities with a tool for landslide-hazard assessment, emergency management, land-use planning, development of early warning systems, and the implementation of public and private policies. The Instituto Nicaragüense de Estudios Territoriales (Nicaraguan Geosciences Institute, INETER) began to compile the database in a digital format in 2003 as part of a comprehensive geographical information system for all types of geohazards. Landslide data have been obtained from a variety of sources including newspapers, technical reports, and landslide inventory maps. Inventory maps are largely based on fieldwork and aerial-photo analyses conducted by foreign development agencies in collaboration with INETER and other Nicaraguan institutions. This paper presents the sources of landslide information, introduces the database, and presents the first analyses of the data at national and regional scales. The database currently contains spatial information for about 17,000 landslides that occurred in mountainous and volcanic terrains. Information is mainly recorded for the period 1826–2003, with a large number of events that occurred during the disastrous Hurricane Mitch in October 1998. The oldest historical event is dated at 1570, some events are recorded as prehistorical, and other events have unknown dates of occurrence. Debris flows have been the most common types of landslides, both in volcanic and nonvolcanic areas, but other types, including rockfalls and slides, have also been identified. Intense and prolonged rainfall, often associated with tropical cyclones, and seismic and volcanic activity represent the most important landslide triggers. At a regional scale, the influence of topographic (elevation, slope angle, slope aspect) and lithologic parameters on the occurrence of landslides was analyzed. The development of the database allowed us to define the state of knowledge on landslide processes in the Nicaragua and to provide a preliminary identification of areas affected by landslides.     

西安金盆水库放水塔附近滑坡特征及成因分析

放水塔附近滑坡是金盆水库右岸原1号滑坡体下游边界段的残留体,受短期强降水因素的诱发导致坡体变形、失稳滑动。滑坡体具顺层牵引滑动特征,且平面上,不同区段变形破坏程度不同,其表现形式与坡体平面旋转有一定的相似性。坡体滑动主要受4组结构面的控制,其中的软弱片理结构面（产状1501703555）与产状为2302903555的另一组结构面构成滑坡的滑动控制面。基岩内发育的大量的软弱片理结构面,大气强降水,滑坡上部相对平缓的地貌及人类工程对地表植被环境的破坏,滑坡体下部喷护层的存在等因素的综合影响,导致了坡体的失稳滑动。     

Remediation and monitoring of a highway across an active landslide at Trevor, North Wales

In March 1989, a moderately steep slope forming sidelong ground below the B5434 Trevor to Froncysyllte Road failed after a period of exceptionally heavy rainfall and severed the highway. A long history of ground movements together with river erosion at the toe and a change in the groundwater regime within the slope created the preconditions for landslide. Site investigations characterised the ground conditions and identified the active slip surface within the compound landslide terrain. The permanent remedial measures adopted proved to be of considerable practical interest involving a bored pile anchored wall with foundations socketed into a deep sandstone foundation stratum. The anchored wall was designed to safeguard the highway and it was anticipated that deloading the slope would reduce the capacity for further movement. The wall achieved the design objective but progressive downslope movements resumed about one year after construction. They continue spasmodically to the present time with rates approaching 9 mm per year both laterally and vertically and although they affect the neighbouring areas, the highway corridor remains intact. The response of load cells attached to the ground anchors reveal two unusual patterns of increasing loads. At the eastern end of the wall, moderate load gains clearly relate to landsliding with reactivation along the existing slip surface and consequent removal of support to the wall as the ground progressively fails in a downslope direction. However, anomalous increases in loads recorded at the western end of the wall have proved more difficult to explain. They may be due either to a build up of water pressure behind the downhill portion of the wall or to localised weak ground conditions or swelling of degraded mudstones behind the wall. Monitoring of the load cells will continue in order to ensure the ongoing integrity of the highway.     

滑坡灾害预测分区的计算机制图

本文在滑坡灾害预测分区的信息模型基础上,重点讨论了灾害预测的计算机制图化的主要过程:因素的数值化,单元边界的确定和彩色图件的绘制。运用中国地质大学计算机系开发的Mapcad系统,在Mv/10000计算机上较好地处理了不规则图幅边界的自然裁剪,不规则单元的输入,以及彩色图件的绘制等问题。     

浙西梅雨滑坡易发性评价模型对比

新疆巩留县广泛发育冻融降雨型滑坡地质灾害,对其现有的研究多考虑降水,而缺乏温度影响的研究,为此,本文特增加了温度因子来进行巩留县滑坡灾害危险性评价。基于巩留县已发生的682个滑坡灾害点,选取坡度、起伏度、坡向、曲率、温度、距断层距离、距河流距离、距道路距离、工程地质岩组等9个评价因子。采用信息量模型(I)、确定性系数模型(CF)、信息量模型+逻辑回归模型(I+LR)以及确定性系数模型+逻辑回归模型(CF+LR)等4种模型对巩留县滑坡危险性进行了评价,划分为极高、高、中和低4个危险等级分区并进行了精度检验与现场实际验证。结果表明：(1)温度对滑坡有较大的触发作用;(2)耦合模型极高、高危险性分区面积明显低于单一模型极高、高危险性分区面积,其中CF+LR模型的极高、高危险性分区面积最小,低危险性分区面积最大;(3)4种模型ROC精度检验AUC值分别为0.889、0.893、0.895和0.900,均能较为客观地评价巩留县滑坡危险性。CF+LR模型精度最高,且经局部地区现场检验,CF+LR模型评价结果与实际情况也最为相符,研究成果对新疆地区巩留县滑坡地质灾害的预防和治理具有一定的借鉴意义。     

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.     

A review of landslide hazards in Japan and assessment of their susceptibility using an analytical hierarchic process (AHP) method

In spite of its small size, Japan suffers many landslide disasters due to intense rainfall and earthquakes. This article describes the distribution and topography of these landslides, and a new method of evaluating the susceptibility, the analytical hierarchic process (AHP). The method assigns scores to each factor of micro-topography of landslide-prone areas identified in aerial photographs, and assesses the susceptibility of landslide from the total score. In addition, a method of simulating sliding mass runout is briefly presented for the designating sediment-related disaster warning areas.     

A modified approach for semi-quantitative estimation of physical vulnerability of buildings exposed to different landslide intensity scenarios

Landslides are the most common natural disasters in mountainous regions, being responsible for significant loss of life as well as damage to critical infrastructure and properties. As the world population grows, people tend to move to higher locations to construct buildings, thereby making structures vulnerable due to landslides. This paper discusses previous research on the vulnerability assessment of structures exposed to landslides and presents a modified semi-quantitative approach to assess the scenario-based physical vulnerability of buildings based on their resistance ability and landslide intensity. Resistance ability is determined by integrating expert knowledge-based resistance factors assigned to five primary building parameters. Landslide intensity matrix defining different intensity levels is proposed based on combinations of landslide velocity and volume. Physical vulnerability of buildings is estimated and classified as class I, II or III for scenario-based low to very high landslide intensity. Finally, the application of the model is illustrated with a case study of 71 buildings from Garhwal Himalayas, India.     

Hydrologic conditions responsible for triggering the Sto e landslide, Slovenia

In two events, on November 15 and 17, 2000, near the Mangart Mountain (2679 m a.s.l.), NW Slovenia, two translational landslides (debris flow slides) with a total volume of more than 1.5 million m³ occurred on the Sto e slope composed of morainic material filled with silt fraction. The first landslide was associated with a dry and the second landslide with a wet debris-flow, respectively. The rain gauging station in the village of Log pod Mangartom recorded 1638.4 mm of rainfall (more than 60% of the average annual precipitation) in the 48 days before the events (rainfall intensity of 1.42 mm/h in 1152 h). The recorded rainfall depth has a recurrence interval of more than 100 years. Other recorded rainfall depths of shorter duration (481.6 mm in 7 days, 174.0 mm in 24 h, 70 mm in 1 h) have recurrence intervals of much less than 100 years. A hydrological analysis of the event showed that the increase in runoff coefficients during the wet period in autumn 2000 before the landslide was as high as two- to threefold. An analysis using natural isotopes of δ¹⁸O and tritium of water samples from the Sto e landslide area has shown permanent but slow exfiltration of underground waters from a reservoir in the slope. In the case of low-intensity and long-duration rainfall in autumn 2000, relatively low permeable (10⁻⁷ m/s) morainic material was nearly saturated but remained stable (average porosity 21%, water content 20%, liquid limit 25%) until high artesian pressures up to 100 m developed in the slope by slow exfiltration from the relatively high permeable (10⁻⁵ m/s) massive dolomite. The Sto e landslide (two debris flow slides) was triggered by high artesian pressures built in the slope after long-duration rainfall. The devastating debris-flows formed from the landslide masses by infiltration of rainfall and surface runoff into the landslide masses and by their liquefaction.     

Site and laboratory investigation of the Slano blato landslide

The Slano blato landslide is situated above the village of Lokavec, in the western part of Slovenia. This area is one of the seismically most active parts of the country. Considering just the last decade, movement of the landslide was observed in November 2000, when the displaced material reached a velocity of 60–100 m/day. Silty and clayey gravel above flysch layers of marl and sandstone formed the landslide mass.Geotechnical investigations of the landslide were performed in 2003 and 2004, when the depth of the landslide was determined, as well as the geotechnical parameters and the sliding mechanism. Rheological tests were also carried out for further analysis. Based on the investigation results and the observed landslide velocity, the landslide was classified as an earth flow. Inclinometer measurements showed that the landslide has two shear surfaces, with different behaviour shown as each.A stability analysis was carried out numerically by applying the Mohr–Coulomb and Burger elasto–plastic models. The Mohr–Coulomb model indicated that the high water level influences the landslide instability. In the case of the Burger elasto-plastic model, a higher velocity was calculated, at a water content of between 35 and 40%.     

A graph deep learning method for landslide displacement prediction based on global navigation satellite system positioning

The accurate prediction of displacement is crucial for landslide deformation monitoring and early warning. This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate landslide displacement prediction method that relies on graph deep learning and Global Navigation Satellite System (GNSS) positioning. First model the graph structure of the monitoring system based on the engineering positions of the GNSS monitoring points and build the adjacent matrix of graph nodes. Then construct the historical and predicted time series feature matrixes using the processed temporal data including GNSS displacement, rainfall, groundwater table and soil moisture content and the graph structure. Last introduce the state-of-the-art graph deep learning GTS (Graph for Time Series) model to improve the accuracy and reliability of landslide displacement prediction which utilizes the temporal-spatial dependency of the monitoring system. This approach outperforms previous studies that only learned temporal features from a single monitoring point and maximally weighs the prediction performance and the priori graph of the monitoring system. The proposed method performs better than SVM, XGBoost, LSTM and DCRNN models in terms of RMSE (1.35 mm), MAE (1.14 mm) and MAPE (0.25) evaluation metrics, which is provided to be effective in future landslide failure early warning.